{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ic4_occAAiAT"
   },
   "source": [
    "##### Copyright 2019 The TensorFlow Authors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cellView": "form",
    "execution": {
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     "iopub.status.busy": "2023-11-08T00:06:16.154481Z",
     "iopub.status.idle": "2023-11-08T00:06:16.158680Z",
     "shell.execute_reply": "2023-11-08T00:06:16.158049Z"
    },
    "id": "ioaprt5q5US7"
   },
   "outputs": [],
   "source": [
    "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "# https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "cellView": "form",
    "execution": {
     "iopub.execute_input": "2023-11-08T00:06:16.162165Z",
     "iopub.status.busy": "2023-11-08T00:06:16.161683Z",
     "iopub.status.idle": "2023-11-08T00:06:16.165232Z",
     "shell.execute_reply": "2023-11-08T00:06:16.164580Z"
    },
    "id": "yCl0eTNH5RS3"
   },
   "outputs": [],
   "source": [
    "#@title MIT License\n",
    "#\n",
    "# Copyright (c) 2017 François Chollet\n",
    "#\n",
    "# Permission is hereby granted, free of charge, to any person obtaining a\n",
    "# copy of this software and associated documentation files (the \"Software\"),\n",
    "# to deal in the Software without restriction, including without limitation\n",
    "# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n",
    "# and/or sell copies of the Software, and to permit persons to whom the\n",
    "# Software is furnished to do so, subject to the following conditions:\n",
    "#\n",
    "# The above copyright notice and this permission notice shall be included in\n",
    "# all copies or substantial portions of the Software.\n",
    "#\n",
    "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
    "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
    "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n",
    "# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
    "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n",
    "# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n",
    "# DEALINGS IN THE SOFTWARE."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ItXfxkxvosLH"
   },
   "source": [
    "# 电影评论文本分类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hKY4XMc9o8iB"
   },
   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>     <a target=\"_blank\" href=\"https://tensorflow.google.cn/tutorials/keras/text_classification\"><img src=\"https://tensorflow.google.cn/images/tf_logo_32px.png\"> 在 TensorFlow.org 上查看</a>   </td>\n",
    "  <td>     <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/zh-cn/tutorials/keras/text_classification.ipynb\"><img src=\"https://tensorflow.google.cn/images/colab_logo_32px.png\">在 Google Colab 中运行</a>   </td>\n",
    "  <td>     <a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/zh-cn/tutorials/keras/text_classification.ipynb\"><img src=\"https://tensorflow.google.cn/images/GitHub-Mark-32px.png\">在 GitHub 上查看源代码</a>   </td>\n",
    "  <td>     <a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/zh-cn/tutorials/keras/text_classification.ipynb\"><img src=\"https://tensorflow.google.cn/images/download_logo_32px.png\">下载笔记本</a> </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Eg62Pmz3o83v"
   },
   "source": [
    "本教程演示了从存储在磁盘上的纯文本文件开始的文本分类。您将训练一个二元分类器对 IMDB 数据集执行情感分析。在笔记本的最后，有一个练习供您尝试，您将在其中训练一个多类分类器来预测 Stack Overflow 上编程问题的标签。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:06:16.169095Z",
     "iopub.status.busy": "2023-11-08T00:06:16.168538Z",
     "iopub.status.idle": "2023-11-08T00:06:19.034768Z",
     "shell.execute_reply": "2023-11-08T00:06:19.033921Z"
    },
    "id": "8RZOuS9LWQvv"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-08 00:06:16.980388: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2023-11-08 00:06:16.980452: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2023-11-08 00:06:16.982190: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import re\n",
    "import shutil\n",
    "import string\n",
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras import losses\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:06:19.039547Z",
     "iopub.status.busy": "2023-11-08T00:06:19.038664Z",
     "iopub.status.idle": "2023-11-08T00:06:19.042939Z",
     "shell.execute_reply": "2023-11-08T00:06:19.042282Z"
    },
    "id": "6-tTFS04dChr"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.15.0-rc1\n"
     ]
    }
   ],
   "source": [
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NBTI1bi8qdFV"
   },
   "source": [
    "## 情感分析\n",
    "\n",
    "此笔记本训练了一个情感分析模型，利用评论文本将电影评论分类为*正面*或*负面*评价。这是一个*二元*（或二类）分类示例，也是一个重要且应用广泛的机器学习问题。\n",
    "\n",
    "您将使用 [Large Movie Review Dataset](https://ai.stanford.edu/~amaas/data/sentiment/)，其中包含 [Internet Movie Database](https://www.imdb.com/) 中的 50,000 条电影评论文本 。我们将这些评论分为两组，其中 25,000 条用于训练，另外 25,000 条用于测试。训练集和测试集是*均衡的*，也就是说其中包含相等数量的正面评价和负面评价。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iAsKG535pHep"
   },
   "source": [
    "### 下载并探索 IMDB 数据集\n",
    "\n",
    "我们下载并提取数据集，然后浏览一下目录结构。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:06:19.046490Z",
     "iopub.status.busy": "2023-11-08T00:06:19.045918Z",
     "iopub.status.idle": "2023-11-08T00:06:59.349596Z",
     "shell.execute_reply": "2023-11-08T00:06:59.348735Z"
    },
    "id": "k7ZYnuajVlFN"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
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    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5021696/84125825 [>.............................] - ETA: 32s"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5300224/84125825 [>.............................] - ETA: 32s"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5480448/84125825 [>.............................] - ETA: 31s"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5758976/84125825 [=>............................] - ETA: 31s"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5955584/84125825 [=>............................] - ETA: 31s"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 6234112/84125825 [=>............................] - ETA: 30s"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 6430720/84125825 [=>............................] - ETA: 30s"
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      " 6709248/84125825 [=>............................] - ETA: 30s"
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      " 6905856/84125825 [=>............................] - ETA: 29s"
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      " 7184384/84125825 [=>............................] - ETA: 29s"
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      " 7397376/84125825 [=>............................] - ETA: 29s"
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      " 7675904/84125825 [=>............................] - ETA: 28s"
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      " 7888896/84125825 [=>............................] - ETA: 28s"
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      " 8167424/84125825 [=>............................] - ETA: 28s"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8380416/84125825 [=>............................] - ETA: 27s"
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      " 8675328/84125825 [==>...........................] - ETA: 27s"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8888320/84125825 [==>...........................] - ETA: 27s"
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      " 9183232/84125825 [==>...........................] - ETA: 27s"
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      " 9396224/84125825 [==>...........................] - ETA: 26s"
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      " 9691136/84125825 [==>...........................] - ETA: 26s"
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      " 9904128/84125825 [==>...........................] - ETA: 26s"
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      "10199040/84125825 [==>...........................] - ETA: 26s"
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      "10428416/84125825 [==>...........................] - ETA: 25s"
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      "10706944/84125825 [==>...........................] - ETA: 25s"
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      "10936320/84125825 [==>...........................] - ETA: 25s"
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      "11231232/84125825 [===>..........................] - ETA: 25s"
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      "11460608/84125825 [===>..........................] - ETA: 24s"
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      "11739136/84125825 [===>..........................] - ETA: 24s"
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      "11968512/84125825 [===>..........................] - ETA: 24s"
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      "12263424/84125825 [===>..........................] - ETA: 24s"
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      "12492800/84125825 [===>..........................] - ETA: 24s"
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      "12771328/84125825 [===>..........................] - ETA: 23s"
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      "12967936/84125825 [===>..........................] - ETA: 23s"
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      "13213696/84125825 [===>..........................] - ETA: 23s"
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      "13492224/84125825 [===>..........................] - ETA: 23s"
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      "13754368/84125825 [===>..........................] - ETA: 23s"
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      "14016512/84125825 [===>..........................] - ETA: 23s"
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      "14262272/84125825 [====>.........................] - ETA: 22s"
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      "14524416/84125825 [====>.........................] - ETA: 22s"
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      "14770176/84125825 [====>.........................] - ETA: 22s"
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      "15048704/84125825 [====>.........................] - ETA: 22s"
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      "15294464/84125825 [====>.........................] - ETA: 22s"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "82829312/84125825 [============================>.] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "83091456/84125825 [============================>.] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "83337216/84125825 [============================>.] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "83615744/84125825 [============================>.] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "83845120/84125825 [============================>.] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "84074496/84125825 [============================>.] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "84125825/84125825 [==============================] - 19s 0us/step\n"
     ]
    }
   ],
   "source": [
    "url = \"https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\"\n",
    "\n",
    "dataset = tf.keras.utils.get_file(\"aclImdb_v1\", url,\n",
    "                                    untar=True, cache_dir='.',\n",
    "                                    cache_subdir='')\n",
    "\n",
    "dataset_dir = os.path.join(os.path.dirname(dataset), 'aclImdb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:06:59.353724Z",
     "iopub.status.busy": "2023-11-08T00:06:59.353410Z",
     "iopub.status.idle": "2023-11-08T00:06:59.360596Z",
     "shell.execute_reply": "2023-11-08T00:06:59.359981Z"
    },
    "id": "355CfOvsV1pl"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['imdb.vocab', 'imdbEr.txt', 'README', 'test', 'train']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(dataset_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:06:59.363856Z",
     "iopub.status.busy": "2023-11-08T00:06:59.363202Z",
     "iopub.status.idle": "2023-11-08T00:06:59.368224Z",
     "shell.execute_reply": "2023-11-08T00:06:59.367563Z"
    },
    "id": "7ASND15oXpF1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['urls_unsup.txt',\n",
       " 'unsupBow.feat',\n",
       " 'urls_neg.txt',\n",
       " 'neg',\n",
       " 'urls_pos.txt',\n",
       " 'labeledBow.feat',\n",
       " 'unsup',\n",
       " 'pos']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dir = os.path.join(dataset_dir, 'train')\n",
    "os.listdir(train_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ysMNMI1CWDFD"
   },
   "source": [
    "`aclImdb/train/pos` 和 `aclImdb/train/neg` 目录包含许多文本文件，每个文件都是一条电影评论。我们来看看其中的一条评论。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:06:59.371538Z",
     "iopub.status.busy": "2023-11-08T00:06:59.370977Z",
     "iopub.status.idle": "2023-11-08T00:06:59.375199Z",
     "shell.execute_reply": "2023-11-08T00:06:59.374543Z"
    },
    "id": "R7g8hFvzWLIZ"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rachel Griffiths writes and directs this award winning short film. A heartwarming story about coping with grief and cherishing the memory of those we've loved and lost. Although, only 15 minutes long, Griffiths manages to capture so much emotion and truth onto film in the short space of time. Bud Tingwell gives a touching performance as Will, a widower struggling to cope with his wife's death. Will is confronted by the harsh reality of loneliness and helplessness as he proceeds to take care of Ruth's pet cow, Tulip. The film displays the grief and responsibility one feels for those they have loved and lost. Good cinematography, great direction, and superbly acted. It will bring tears to all those who have lost a loved one, and survived.\n"
     ]
    }
   ],
   "source": [
    "sample_file = os.path.join(train_dir, 'pos/1181_9.txt')\n",
    "with open(sample_file) as f:\n",
    "  print(f.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Mk20TEm6ZRFP"
   },
   "source": [
    "### 加载数据集\n",
    "\n",
    "接下来，您将从磁盘加载数据并将其准备为适合训练的格式。为此，您将使用有用的 [text_dataset_from_directory](https://tensorflow.google.cn/api_docs/python/tf/keras/preprocessing/text_dataset_from_directory) 实用工具，它期望的目录结构如下所示。\n",
    "\n",
    "```\n",
    "main_directory/\n",
    "...class_a/\n",
    "......a_text_1.txt\n",
    "......a_text_2.txt\n",
    "...class_b/\n",
    "......b_text_1.txt\n",
    "......b_text_2.txt\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nQauv38Lnok3"
   },
   "source": [
    "要准备用于二元分类的数据集，磁盘上需要有两个文件夹，分别对应于 `class_a` 和 `class_b`。这些将是正面和负面的电影评论，可以在 `aclImdb/train/pos` 和 `aclImdb/train/neg` 中找到。由于 IMDB 数据集包含其他文件夹，因此您需要在使用此实用工具之前将其移除。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:06:59.378678Z",
     "iopub.status.busy": "2023-11-08T00:06:59.378111Z",
     "iopub.status.idle": "2023-11-08T00:07:00.250491Z",
     "shell.execute_reply": "2023-11-08T00:07:00.249739Z"
    },
    "id": "VhejsClzaWfl"
   },
   "outputs": [],
   "source": [
    "remove_dir = os.path.join(train_dir, 'unsup')\n",
    "shutil.rmtree(remove_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "95kkUdRoaeMw"
   },
   "source": [
    "接下来，您将使用 `text_dataset_from_directory` 实用工具创建带标签的 `tf.data.Dataset`。[tf.data](https://tensorflow.google.cn/guide/data) 是一组强大的数据处理工具。\n",
    "\n",
    "运行机器学习实验时，最佳做法是将数据集拆成三份：[训练](https://developers.google.com/machine-learning/glossary#training_set)、[验证](https://developers.google.com/machine-learning/glossary#validation_set) 和 [测试](https://developers.google.com/machine-learning/glossary#test-set)。\n",
    "\n",
    "IMDB 数据集已经分成训练集和测试集，但缺少验证集。我们来通过下面的 `validation_split` 参数，使用 80:20 拆分训练数据来创建验证集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:07:00.255167Z",
     "iopub.status.busy": "2023-11-08T00:07:00.254452Z",
     "iopub.status.idle": "2023-11-08T00:07:04.840458Z",
     "shell.execute_reply": "2023-11-08T00:07:04.839428Z"
    },
    "id": "nOrK-MTYaw3C"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 25000 files belonging to 2 classes.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using 20000 files for training.\n"
     ]
    }
   ],
   "source": [
    "batch_size = 32\n",
    "seed = 42\n",
    "\n",
    "raw_train_ds = tf.keras.utils.text_dataset_from_directory(\n",
    "    'aclImdb/train', \n",
    "    batch_size=batch_size, \n",
    "    validation_split=0.2, \n",
    "    subset='training', \n",
    "    seed=seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5Y33oxOUpYkh"
   },
   "source": [
    "如上所示，训练文件夹中有 25,000 个样本，您将使用其中的 80%（或 20,000 个）进行训练。稍后您将看到，您可以通过将数据集直接传递给 `model.fit` 来训练模型。如果您不熟悉 `tf.data`，还可以遍历数据集并打印出一些样本，如下所示。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:07:04.845302Z",
     "iopub.status.busy": "2023-11-08T00:07:04.844561Z",
     "iopub.status.idle": "2023-11-08T00:07:04.880991Z",
     "shell.execute_reply": "2023-11-08T00:07:04.880202Z"
    },
    "id": "51wNaPPApk1K"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Review b'\"Pandemonium\" is a horror movie spoof that comes off more stupid than funny. Believe me when I tell you, I love comedies. Especially comedy spoofs. \"Airplane\", \"The Naked Gun\" trilogy, \"Blazing Saddles\", \"High Anxiety\", and \"Spaceballs\" are some of my favorite comedies that spoof a particular genre. \"Pandemonium\" is not up there with those films. Most of the scenes in this movie had me sitting there in stunned silence because the movie wasn\\'t all that funny. There are a few laughs in the film, but when you watch a comedy, you expect to laugh a lot more than a few times and that\\'s all this film has going for it. Geez, \"Scream\" had more laughs than this film and that was more of a horror film. How bizarre is that?<br /><br />*1/2 (out of four)'\n",
      "Label 0\n",
      "Review b\"David Mamet is a very interesting and a very un-equal director. His first movie 'House of Games' was the one I liked best, and it set a series of films with characters whose perspective of life changes as they get into complicated situations, and so does the perspective of the viewer.<br /><br />So is 'Homicide' which from the title tries to set the mind of the viewer to the usual crime drama. The principal characters are two cops, one Jewish and one Irish who deal with a racially charged area. The murder of an old Jewish shop owner who proves to be an ancient veteran of the Israeli Independence war triggers the Jewish identity in the mind and heart of the Jewish detective.<br /><br />This is were the flaws of the film are the more obvious. The process of awakening is theatrical and hard to believe, the group of Jewish militants is operatic, and the way the detective eventually walks to the final violent confrontation is pathetic. The end of the film itself is Mamet-like smart, but disappoints from a human emotional perspective.<br /><br />Joe Mantegna and William Macy give strong performances, but the flaws of the story are too evident to be easily compensated.\"\n",
      "Label 0\n",
      "Review b'Great documentary about the lives of NY firefighters during the worst terrorist attack of all time.. That reason alone is why this should be a must see collectors item.. What shocked me was not only the attacks, but the\"High Fat Diet\" and physical appearance of some of these firefighters. I think a lot of Doctors would agree with me that,in the physical shape they were in, some of these firefighters would NOT of made it to the 79th floor carrying over 60 lbs of gear. Having said that i now have a greater respect for firefighters and i realize becoming a firefighter is a life altering job. The French have a history of making great documentary\\'s and that is what this is, a Great Documentary.....'\n",
      "Label 1\n"
     ]
    }
   ],
   "source": [
    "for text_batch, label_batch in raw_train_ds.take(1):\n",
    "  for i in range(3):\n",
    "    print(\"Review\", text_batch.numpy()[i])\n",
    "    print(\"Label\", label_batch.numpy()[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JWq1SUIrp1a-"
   },
   "source": [
    "请注意，评论包含原始文本（带有标点符号和偶尔出现的 HTML 代码，如 `<br/>`）。我们将在以下部分展示如何处理这些问题。\n",
    "\n",
    "标签为 0 或 1。要查看它们与正面和负面电影评论的对应关系，可以查看数据集上的 `class_names` 属性。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:07:04.884996Z",
     "iopub.status.busy": "2023-11-08T00:07:04.884209Z",
     "iopub.status.idle": "2023-11-08T00:07:04.888550Z",
     "shell.execute_reply": "2023-11-08T00:07:04.887775Z"
    },
    "id": "MlICTG8spyO2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label 0 corresponds to neg\n",
      "Label 1 corresponds to pos\n"
     ]
    }
   ],
   "source": [
    "print(\"Label 0 corresponds to\", raw_train_ds.class_names[0])\n",
    "print(\"Label 1 corresponds to\", raw_train_ds.class_names[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pbdO39vYqdJr"
   },
   "source": [
    "接下来，您将创建验证数据集和测试数据集。您将使用训练集中剩余的 5,000 条评论进行验证。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SzxazN8Hq1pF"
   },
   "source": [
    "注：使用 `validation_split` 和 `subset` 参数时，请确保要么指定随机种子，要么传递 `shuffle=False`，这样验证拆分和训练拆分就不会重叠。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:07:04.892296Z",
     "iopub.status.busy": "2023-11-08T00:07:04.891639Z",
     "iopub.status.idle": "2023-11-08T00:07:06.118029Z",
     "shell.execute_reply": "2023-11-08T00:07:06.117313Z"
    },
    "id": "JsMwwhOoqjKF"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 25000 files belonging to 2 classes.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using 5000 files for validation.\n"
     ]
    }
   ],
   "source": [
    "raw_val_ds = tf.keras.utils.text_dataset_from_directory(\n",
    "    'aclImdb/train', \n",
    "    batch_size=batch_size, \n",
    "    validation_split=0.2, \n",
    "    subset='validation', \n",
    "    seed=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:07:06.121679Z",
     "iopub.status.busy": "2023-11-08T00:07:06.121425Z",
     "iopub.status.idle": "2023-11-08T00:07:07.398188Z",
     "shell.execute_reply": "2023-11-08T00:07:07.397398Z"
    },
    "id": "rdSr0Nt3q_ns"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 25000 files belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "raw_test_ds = tf.keras.utils.text_dataset_from_directory(\n",
    "    'aclImdb/test', \n",
    "    batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qJmTiO0IYAjm"
   },
   "source": [
    "### 准备用于训练的数据集\n",
    "\n",
    "接下来，您将使用有用的 `tf.keras.layers.TextVectorization` 层对数据进行标准化、词例化和向量化。\n",
    "\n",
    "标准化是指对文本进行预处理，通常是移除标点符号或 HTML 元素以简化数据集。词例化是指将字符串分割成词例（例如，通过空格将句子分割成单个单词）。向量化是指将词例转换为数字，以便将它们输入神经网络。所有这些任务都可以通过这个层完成。\n",
    "\n",
    "正如您在上面看到的，评论包含各种 HTML 代码，例如 `<br />`。`TextVectorization` 层（默认情况下会将文本转换为小写并去除标点符号，但不会去除 HTML）中的默认标准化程序不会移除这些代码。您将编写一个自定义标准化函数来移除 HTML。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZVcHl-SLrH-u"
   },
   "source": [
    "注：为了防止[训练-测试偏差](https://developers.google.com/machine-learning/guides/rules-of-ml#training-serving_skew)（也称为训练-应用偏差），在训练和测试时间对数据进行相同的预处理非常重要。为此，可以将 `TextVectorization` 层直接包含在模型中，如本教程后面所示。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:07:07.402607Z",
     "iopub.status.busy": "2023-11-08T00:07:07.402334Z",
     "iopub.status.idle": "2023-11-08T00:07:07.406673Z",
     "shell.execute_reply": "2023-11-08T00:07:07.406003Z"
    },
    "id": "SDRI_s_tX1Hk"
   },
   "outputs": [],
   "source": [
    "def custom_standardization(input_data):\n",
    "  lowercase = tf.strings.lower(input_data)\n",
    "  stripped_html = tf.strings.regex_replace(lowercase, '<br />', ' ')\n",
    "  return tf.strings.regex_replace(stripped_html,\n",
    "                                  '[%s]' % re.escape(string.punctuation),\n",
    "                                  '')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d2d3Aw8dsUux"
   },
   "source": [
    "<br>接下来，您将创建一个 `TextVectorization` 层。您将使用该层对我们的数据进行标准化、词例化和向量化。您将 `output_mode` 设置为 `int` 以便为每个词例创建唯一的整数索引。\n",
    "\n",
    "请注意，您使用的是默认拆分函数，以及您在上面定义的自定义标准化函数。您还将为模型定义一些常量，例如显式的最大 `sequence_length`，这会使层将序列填充或截断为精确的 `sequence_length` 值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:07:07.410143Z",
     "iopub.status.busy": "2023-11-08T00:07:07.409843Z",
     "iopub.status.idle": "2023-11-08T00:07:07.425602Z",
     "shell.execute_reply": "2023-11-08T00:07:07.424907Z"
    },
    "id": "-c76RvSzsMnX"
   },
   "outputs": [],
   "source": [
    "max_features = 10000\n",
    "sequence_length = 250\n",
    "\n",
    "vectorize_layer = layers.TextVectorization(\n",
    "    standardize=custom_standardization,\n",
    "    max_tokens=max_features,\n",
    "    output_mode='int',\n",
    "    output_sequence_length=sequence_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vlFOpfF6scT6"
   },
   "source": [
    "接下来，您将调用 `adapt` 以使预处理层的状态适合数据集。这会使模型构建字符串到整数的索引。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lAhdjK7AtroA"
   },
   "source": [
    "注：在调用时请务必仅使用您的训练数据（使用测试集会泄漏信息）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:07:07.429658Z",
     "iopub.status.busy": "2023-11-08T00:07:07.428989Z",
     "iopub.status.idle": "2023-11-08T00:07:09.966421Z",
     "shell.execute_reply": "2023-11-08T00:07:09.965506Z"
    },
    "id": "GH4_2ZGJsa_X"
   },
   "outputs": [],
   "source": [
    "# Make a text-only dataset (without labels), then call adapt\n",
    "train_text = raw_train_ds.map(lambda x, y: x)\n",
    "vectorize_layer.adapt(train_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SHQVEFzNt-K_"
   },
   "source": [
    "我们来创建一个函数来查看使用该层预处理一些数据的结果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:07:09.971004Z",
     "iopub.status.busy": "2023-11-08T00:07:09.970348Z",
     "iopub.status.idle": "2023-11-08T00:07:09.974465Z",
     "shell.execute_reply": "2023-11-08T00:07:09.973774Z"
    },
    "id": "SCIg_T50wOCU"
   },
   "outputs": [],
   "source": [
    "def vectorize_text(text, label):\n",
    "  text = tf.expand_dims(text, -1)\n",
    "  return vectorize_layer(text), label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:07:09.977759Z",
     "iopub.status.busy": "2023-11-08T00:07:09.977506Z",
     "iopub.status.idle": "2023-11-08T00:07:10.048355Z",
     "shell.execute_reply": "2023-11-08T00:07:10.047585Z"
    },
    "id": "XULcm6B3xQIO"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Review tf.Tensor(b'Great movie - especially the music - Etta James - \"At Last\". This speaks volumes when you have finally found that special someone.', shape=(), dtype=string)\n",
      "Label neg\n",
      "Vectorized review (<tf.Tensor: shape=(1, 250), dtype=int64, numpy=\n",
      "array([[  86,   17,  260,    2,  222,    1,  571,   31,  229,   11, 2418,\n",
      "           1,   51,   22,   25,  404,  251,   12,  306,  282,    0,    0,\n",
      "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
      "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
      "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
      "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
      "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
      "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
      "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
      "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
      "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
      "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
      "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
      "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
      "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
      "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
      "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
      "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
      "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
      "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
      "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
      "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
      "           0,    0,    0,    0,    0,    0,    0,    0]])>, <tf.Tensor: shape=(), dtype=int32, numpy=0>)\n"
     ]
    }
   ],
   "source": [
    "# retrieve a batch (of 32 reviews and labels) from the dataset\n",
    "text_batch, label_batch = next(iter(raw_train_ds))\n",
    "first_review, first_label = text_batch[0], label_batch[0]\n",
    "print(\"Review\", first_review)\n",
    "print(\"Label\", raw_train_ds.class_names[first_label])\n",
    "print(\"Vectorized review\", vectorize_text(first_review, first_label))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6u5EX0hxyNZT"
   },
   "source": [
    "正如您在上面看到的，每个词例都被一个整数替换了。您可以通过在该层上调用 `.get_vocabulary()` 来查找每个整数对应的词例（字符串）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:07:10.051939Z",
     "iopub.status.busy": "2023-11-08T00:07:10.051643Z",
     "iopub.status.idle": "2023-11-08T00:07:10.101341Z",
     "shell.execute_reply": "2023-11-08T00:07:10.100607Z"
    },
    "id": "kRq9hTQzhVhW"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1287 --->  silent\n",
      " 313 --->  night\n",
      "Vocabulary size: 10000\n"
     ]
    }
   ],
   "source": [
    "print(\"1287 ---> \",vectorize_layer.get_vocabulary()[1287])\n",
    "print(\" 313 ---> \",vectorize_layer.get_vocabulary()[313])\n",
    "print('Vocabulary size: {}'.format(len(vectorize_layer.get_vocabulary())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XD2H6utRydGv"
   },
   "source": [
    "差不多可以训练您的模型了。作为最后的预处理步骤，将之前创建的 TextVectorization 层应用于训练数据集、验证数据集和测试数据集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:07:10.104863Z",
     "iopub.status.busy": "2023-11-08T00:07:10.104588Z",
     "iopub.status.idle": "2023-11-08T00:07:10.240596Z",
     "shell.execute_reply": "2023-11-08T00:07:10.239797Z"
    },
    "id": "2zhmpeViI1iG"
   },
   "outputs": [],
   "source": [
    "train_ds = raw_train_ds.map(vectorize_text)\n",
    "val_ds = raw_val_ds.map(vectorize_text)\n",
    "test_ds = raw_test_ds.map(vectorize_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YsVQyPMizjuO"
   },
   "source": [
    "### 配置数据集以提高性能\n",
    "\n",
    "以下是加载数据时应该使用的两种重要方法，以确保 I/O 不会阻塞。\n",
    "\n",
    "从磁盘加载后，`.cache()` 会将数据保存在内存中。这将确保数据集在训练模型时不会成为瓶颈。如果您的数据集太大而无法放入内存，也可以使用此方法创建高性能的磁盘缓存，这比许多小文件的读取效率更高。\n",
    "\n",
    "`prefetch()` 会在训练时将数据预处理和模型执行重叠。\n",
    "\n",
    "您可以在[数据性能指南](https://tensorflow.google.cn/guide/data_performance)中深入了解这两种方法，以及如何将数据缓存到磁盘。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:07:10.244768Z",
     "iopub.status.busy": "2023-11-08T00:07:10.244493Z",
     "iopub.status.idle": "2023-11-08T00:07:10.257482Z",
     "shell.execute_reply": "2023-11-08T00:07:10.256763Z"
    },
    "id": "wMcs_H7izm5m"
   },
   "outputs": [],
   "source": [
    "AUTOTUNE = tf.data.AUTOTUNE\n",
    "\n",
    "train_ds = train_ds.cache().prefetch(buffer_size=AUTOTUNE)\n",
    "val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)\n",
    "test_ds = test_ds.cache().prefetch(buffer_size=AUTOTUNE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LLC02j2g-llC"
   },
   "source": [
    "### 创建模型\n",
    "\n",
    "是时候创建您的神经网络了："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:07:10.261022Z",
     "iopub.status.busy": "2023-11-08T00:07:10.260760Z",
     "iopub.status.idle": "2023-11-08T00:07:10.264087Z",
     "shell.execute_reply": "2023-11-08T00:07:10.263392Z"
    },
    "id": "dkQP6in8yUBR"
   },
   "outputs": [],
   "source": [
    "embedding_dim = 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:07:10.267425Z",
     "iopub.status.busy": "2023-11-08T00:07:10.267153Z",
     "iopub.status.idle": "2023-11-08T00:07:10.336069Z",
     "shell.execute_reply": "2023-11-08T00:07:10.335303Z"
    },
    "id": "xpKOoWgu-llD"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Layer (type)                Output Shape              Param #   \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=================================================================\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " embedding (Embedding)       (None, None, 16)          160016    \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " dropout (Dropout)           (None, None, 16)          0         \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " global_average_pooling1d (  (None, 16)                0         \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " GlobalAveragePooling1D)                                         \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " dropout_1 (Dropout)         (None, 16)                0         \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " dense (Dense)               (None, 1)                 17        \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=================================================================\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total params: 160033 (625.13 KB)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trainable params: 160033 (625.13 KB)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Non-trainable params: 0 (0.00 Byte)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.Sequential([\n",
    "  layers.Embedding(max_features + 1, embedding_dim),\n",
    "  layers.Dropout(0.2),\n",
    "  layers.GlobalAveragePooling1D(),\n",
    "  layers.Dropout(0.2),\n",
    "  layers.Dense(1)])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6PbKQ6mucuKL"
   },
   "source": [
    "层按顺序堆叠以构建分类器：\n",
    "\n",
    "1. 第一个层是 `Embedding` 层。此层采用整数编码的评论，并查找每个单词索引的嵌入向量。这些向量是通过模型训练学习到的。向量向输出数组增加了一个维度。得到的维度为：`(batch, sequence, embedding)`。要详细了解嵌入向量，请参阅[单词嵌入向量](https://tensorflow.google.cn/text/guide/word_embeddings)教程。\n",
    "2. 接下来，`GlobalAveragePooling1D` 将通过对序列维度求平均值来为每个样本返回一个定长输出向量。这允许模型以尽可能最简单的方式处理变长输入。\n",
    "3. 该定长输出向量通过一个有 16 个隐层单元的全连接（`Dense`）层传输。\n",
    "4. 最后一层与单个输出结点密集连接。使用 `Sigmoid` 激活函数，其函数值为介于 0 与 1 之间的浮点数，表示概率或置信度。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L4EqVWg4-llM"
   },
   "source": [
    "### 损失函数与优化器\n",
    "\n",
    "一个模型需要损失函数和优化器来进行训练。由于这是一个二分类问题且模型输出概率值（一个使用 sigmoid 激活函数的单一单元层），我们将使用 `binary_crossentropy` 损失函数。\n",
    "\n",
    "这不是损失函数的唯一选择，例如，您可以选择 `mean_squared_error` 。但是，一般来说 `binary_crossentropy` 更适合处理概率——它能够度量概率分布之间的“距离”，或者在我们的示例中，指的是度量 ground-truth 分布与预测值之间的“距离”。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:07:10.346063Z",
     "iopub.status.busy": "2023-11-08T00:07:10.345767Z",
     "iopub.status.idle": "2023-11-08T00:07:10.366284Z",
     "shell.execute_reply": "2023-11-08T00:07:10.365559Z"
    },
    "id": "Mr0GP-cQ-llN"
   },
   "outputs": [],
   "source": [
    "model.compile(loss=losses.BinaryCrossentropy(from_logits=True),\n",
    "              optimizer='adam',\n",
    "              metrics=tf.metrics.BinaryAccuracy(threshold=0.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "35jv_fzP-llU"
   },
   "source": [
    "### 训练模型\n",
    "\n",
    "以 512 个样本的 mini-batch 大小迭代 40 个 epoch 来训练模型。这是指对 `x_train` 和 `y_train` 张量中所有样本的的 40 次迭代。在训练过程中，监测来自验证集的 10,000 个样本上的损失值（loss）和准确率（accuracy）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:07:10.370041Z",
     "iopub.status.busy": "2023-11-08T00:07:10.369560Z",
     "iopub.status.idle": "2023-11-08T00:08:18.123086Z",
     "shell.execute_reply": "2023-11-08T00:08:18.122257Z"
    },
    "id": "tXSGrjWZ-llW"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1699402032.417606  600522 device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/625 [..............................] - ETA: 24:22 - loss: 0.6924 - binary_accuracy: 0.5625"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  2/625 [..............................] - ETA: 1:42 - loss: 0.6936 - binary_accuracy: 0.4375 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  3/625 [..............................] - ETA: 1:42 - loss: 0.6929 - binary_accuracy: 0.4896"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  4/625 [..............................] - ETA: 1:41 - loss: 0.6928 - binary_accuracy: 0.5078"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  5/625 [..............................] - ETA: 1:40 - loss: 0.6922 - binary_accuracy: 0.5375"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  6/625 [..............................] - ETA: 1:40 - loss: 0.6922 - binary_accuracy: 0.5469"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  7/625 [..............................] - ETA: 1:40 - loss: 0.6926 - binary_accuracy: 0.5312"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  8/625 [..............................] - ETA: 1:40 - loss: 0.6929 - binary_accuracy: 0.5195"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  9/625 [..............................] - ETA: 1:40 - loss: 0.6929 - binary_accuracy: 0.5104"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 10/625 [..............................] - ETA: 1:40 - loss: 0.6931 - binary_accuracy: 0.5031"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 11/625 [..............................] - ETA: 1:40 - loss: 0.6930 - binary_accuracy: 0.5085"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 12/625 [..............................] - ETA: 1:39 - loss: 0.6929 - binary_accuracy: 0.5078"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 13/625 [..............................] - ETA: 1:39 - loss: 0.6929 - binary_accuracy: 0.5072"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 14/625 [..............................] - ETA: 1:39 - loss: 0.6931 - binary_accuracy: 0.5045"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 15/625 [..............................] - ETA: 1:39 - loss: 0.6931 - binary_accuracy: 0.5083"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/625 [..............................] - ETA: 1:39 - loss: 0.6929 - binary_accuracy: 0.5176"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 17/625 [..............................] - ETA: 1:38 - loss: 0.6931 - binary_accuracy: 0.5092"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 18/625 [..............................] - ETA: 1:38 - loss: 0.6931 - binary_accuracy: 0.5087"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 19/625 [..............................] - ETA: 1:38 - loss: 0.6932 - binary_accuracy: 0.5033"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 21/625 [>.............................] - ETA: 1:33 - loss: 0.6932 - binary_accuracy: 0.5074"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 22/625 [>.............................] - ETA: 1:33 - loss: 0.6932 - binary_accuracy: 0.5085"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 23/625 [>.............................] - ETA: 1:33 - loss: 0.6931 - binary_accuracy: 0.5136"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 24/625 [>.............................] - ETA: 1:33 - loss: 0.6932 - binary_accuracy: 0.5091"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 25/625 [>.............................] - ETA: 1:33 - loss: 0.6932 - binary_accuracy: 0.5088"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 26/625 [>.............................] - ETA: 1:33 - loss: 0.6931 - binary_accuracy: 0.5120"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 27/625 [>.............................] - ETA: 1:33 - loss: 0.6932 - binary_accuracy: 0.5035"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 28/625 [>.............................] - ETA: 1:33 - loss: 0.6932 - binary_accuracy: 0.5045"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 29/625 [>.............................] - ETA: 1:33 - loss: 0.6932 - binary_accuracy: 0.5065"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 30/625 [>.............................] - ETA: 1:33 - loss: 0.6932 - binary_accuracy: 0.5073"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 31/625 [>.............................] - ETA: 1:33 - loss: 0.6932 - binary_accuracy: 0.5050"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/625 [>.............................] - ETA: 1:33 - loss: 0.6932 - binary_accuracy: 0.5049"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 33/625 [>.............................] - ETA: 1:33 - loss: 0.6931 - binary_accuracy: 0.5066"
     ]
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    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 35/625 [>.............................] - ETA: 1:30 - loss: 0.6931 - binary_accuracy: 0.5071"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 36/625 [>.............................] - ETA: 1:30 - loss: 0.6930 - binary_accuracy: 0.5087"
     ]
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    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 37/625 [>.............................] - ETA: 1:30 - loss: 0.6930 - binary_accuracy: 0.5076"
     ]
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    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 38/625 [>.............................] - ETA: 1:30 - loss: 0.6930 - binary_accuracy: 0.5074"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 39/625 [>.............................] - ETA: 1:30 - loss: 0.6930 - binary_accuracy: 0.5088"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 40/625 [>.............................] - ETA: 1:30 - loss: 0.6929 - binary_accuracy: 0.5148"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 41/625 [>.............................] - ETA: 1:30 - loss: 0.6929 - binary_accuracy: 0.5183"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 42/625 [=>............................] - ETA: 1:30 - loss: 0.6928 - binary_accuracy: 0.5223"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 43/625 [=>............................] - ETA: 1:30 - loss: 0.6928 - binary_accuracy: 0.5247"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 44/625 [=>............................] - ETA: 1:30 - loss: 0.6927 - binary_accuracy: 0.5305"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 46/625 [=>............................] - ETA: 1:27 - loss: 0.6926 - binary_accuracy: 0.5333"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 47/625 [=>............................] - ETA: 1:27 - loss: 0.6926 - binary_accuracy: 0.5366"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 48/625 [=>............................] - ETA: 1:27 - loss: 0.6925 - binary_accuracy: 0.5391"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 49/625 [=>............................] - ETA: 1:27 - loss: 0.6925 - binary_accuracy: 0.5427"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 50/625 [=>............................] - ETA: 1:27 - loss: 0.6925 - binary_accuracy: 0.5425"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 51/625 [=>............................] - ETA: 1:27 - loss: 0.6925 - binary_accuracy: 0.5423"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 52/625 [=>............................] - ETA: 1:27 - loss: 0.6924 - binary_accuracy: 0.5445"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 54/625 [=>............................] - ETA: 1:26 - loss: 0.6923 - binary_accuracy: 0.5480"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 55/625 [=>............................] - ETA: 1:26 - loss: 0.6923 - binary_accuracy: 0.5472"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 56/625 [=>............................] - ETA: 1:26 - loss: 0.6923 - binary_accuracy: 0.5463"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 57/625 [=>............................] - ETA: 1:26 - loss: 0.6922 - binary_accuracy: 0.5461"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 58/625 [=>............................] - ETA: 1:26 - loss: 0.6922 - binary_accuracy: 0.5463"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 59/625 [=>............................] - ETA: 1:26 - loss: 0.6922 - binary_accuracy: 0.5456"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 60/625 [=>............................] - ETA: 1:25 - loss: 0.6922 - binary_accuracy: 0.5469"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 61/625 [=>............................] - ETA: 1:25 - loss: 0.6922 - binary_accuracy: 0.5461"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 62/625 [=>............................] - ETA: 1:25 - loss: 0.6920 - binary_accuracy: 0.5484"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 63/625 [==>...........................] - ETA: 1:25 - loss: 0.6920 - binary_accuracy: 0.5481"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 64/625 [==>...........................] - ETA: 1:25 - loss: 0.6920 - binary_accuracy: 0.5469"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 65/625 [==>...........................] - ETA: 1:25 - loss: 0.6920 - binary_accuracy: 0.5471"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 68/625 [==>...........................] - ETA: 1:22 - loss: 0.6920 - binary_accuracy: 0.5437"
     ]
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    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 70/625 [==>...........................] - ETA: 1:21 - loss: 0.6920 - binary_accuracy: 0.5424"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 71/625 [==>...........................] - ETA: 1:21 - loss: 0.6920 - binary_accuracy: 0.5423"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 73/625 [==>...........................] - ETA: 1:20 - loss: 0.6920 - binary_accuracy: 0.5407"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 74/625 [==>...........................] - ETA: 1:19 - loss: 0.6919 - binary_accuracy: 0.5414"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 76/625 [==>...........................] - ETA: 1:18 - loss: 0.6918 - binary_accuracy: 0.5432"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 77/625 [==>...........................] - ETA: 1:18 - loss: 0.6917 - binary_accuracy: 0.5422"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 78/625 [==>...........................] - ETA: 1:18 - loss: 0.6918 - binary_accuracy: 0.5397"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 79/625 [==>...........................] - ETA: 1:18 - loss: 0.6917 - binary_accuracy: 0.5407"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 80/625 [==>...........................] - ETA: 1:18 - loss: 0.6917 - binary_accuracy: 0.5410"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 83/625 [==>...........................] - ETA: 1:16 - loss: 0.6917 - binary_accuracy: 0.5384"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 84/625 [===>..........................] - ETA: 1:16 - loss: 0.6917 - binary_accuracy: 0.5387"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 85/625 [===>..........................] - ETA: 1:16 - loss: 0.6917 - binary_accuracy: 0.5379"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 86/625 [===>..........................] - ETA: 1:16 - loss: 0.6916 - binary_accuracy: 0.5385"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 88/625 [===>..........................] - ETA: 1:15 - loss: 0.6916 - binary_accuracy: 0.5376"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 89/625 [===>..........................] - ETA: 1:15 - loss: 0.6917 - binary_accuracy: 0.5351"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 90/625 [===>..........................] - ETA: 1:15 - loss: 0.6917 - binary_accuracy: 0.5344"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 91/625 [===>..........................] - ETA: 1:15 - loss: 0.6917 - binary_accuracy: 0.5340"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 93/625 [===>..........................] - ETA: 1:14 - loss: 0.6917 - binary_accuracy: 0.5319"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 96/625 [===>..........................] - ETA: 1:12 - loss: 0.6918 - binary_accuracy: 0.5306"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 98/625 [===>..........................] - ETA: 1:11 - loss: 0.6917 - binary_accuracy: 0.5306"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "101/625 [===>..........................] - ETA: 1:10 - loss: 0.6917 - binary_accuracy: 0.5269"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "102/625 [===>..........................] - ETA: 1:10 - loss: 0.6917 - binary_accuracy: 0.5263"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "104/625 [===>..........................] - ETA: 1:09 - loss: 0.6917 - binary_accuracy: 0.5264"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "105/625 [====>.........................] - ETA: 1:09 - loss: 0.6917 - binary_accuracy: 0.5259"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "107/625 [====>.........................] - ETA: 1:08 - loss: 0.6917 - binary_accuracy: 0.5260"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "108/625 [====>.........................] - ETA: 1:08 - loss: 0.6916 - binary_accuracy: 0.5275"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "109/625 [====>.........................] - ETA: 1:08 - loss: 0.6916 - binary_accuracy: 0.5284"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "110/625 [====>.........................] - ETA: 1:08 - loss: 0.6916 - binary_accuracy: 0.5290"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "111/625 [====>.........................] - ETA: 1:08 - loss: 0.6916 - binary_accuracy: 0.5293"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "112/625 [====>.........................] - ETA: 1:08 - loss: 0.6916 - binary_accuracy: 0.5293"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "131/625 [=====>........................] - ETA: 1:03 - loss: 0.6913 - binary_accuracy: 0.5441"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "134/625 [=====>........................] - ETA: 1:02 - loss: 0.6912 - binary_accuracy: 0.5480"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "217/625 [=========>....................] - ETA: 47s - loss: 0.6893 - binary_accuracy: 0.5936"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "219/625 [=========>....................] - ETA: 46s - loss: 0.6893 - binary_accuracy: 0.5945"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "220/625 [=========>....................] - ETA: 46s - loss: 0.6893 - binary_accuracy: 0.5945"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "221/625 [=========>....................] - ETA: 46s - loss: 0.6892 - binary_accuracy: 0.5952"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "224/625 [=========>....................] - ETA: 46s - loss: 0.6891 - binary_accuracy: 0.5968"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "225/625 [=========>....................] - ETA: 46s - loss: 0.6891 - binary_accuracy: 0.5974"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "228/625 [=========>....................] - ETA: 45s - loss: 0.6890 - binary_accuracy: 0.5979"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "233/625 [==========>...................] - ETA: 44s - loss: 0.6888 - binary_accuracy: 0.6005"
     ]
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "235/625 [==========>...................] - ETA: 44s - loss: 0.6888 - binary_accuracy: 0.6008"
     ]
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "237/625 [==========>...................] - ETA: 43s - loss: 0.6887 - binary_accuracy: 0.6015"
     ]
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "238/625 [==========>...................] - ETA: 43s - loss: 0.6886 - binary_accuracy: 0.6024"
     ]
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "240/625 [==========>...................] - ETA: 43s - loss: 0.6886 - binary_accuracy: 0.6029"
     ]
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "241/625 [==========>...................] - ETA: 43s - loss: 0.6885 - binary_accuracy: 0.6035"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "244/625 [==========>...................] - ETA: 42s - loss: 0.6884 - binary_accuracy: 0.6049"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "245/625 [==========>...................] - ETA: 42s - loss: 0.6884 - binary_accuracy: 0.6055"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "247/625 [==========>...................] - ETA: 42s - loss: 0.6883 - binary_accuracy: 0.6063"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "249/625 [==========>...................] - ETA: 42s - loss: 0.6883 - binary_accuracy: 0.6072"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "251/625 [===========>..................] - ETA: 41s - loss: 0.6882 - binary_accuracy: 0.6082"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "253/625 [===========>..................] - ETA: 41s - loss: 0.6882 - binary_accuracy: 0.6089"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "254/625 [===========>..................] - ETA: 41s - loss: 0.6881 - binary_accuracy: 0.6094"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "256/625 [===========>..................] - ETA: 41s - loss: 0.6881 - binary_accuracy: 0.6102"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "258/625 [===========>..................] - ETA: 40s - loss: 0.6880 - binary_accuracy: 0.6114"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "259/625 [===========>..................] - ETA: 40s - loss: 0.6879 - binary_accuracy: 0.6120"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "264/625 [===========>..................] - ETA: 40s - loss: 0.6877 - binary_accuracy: 0.6148"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "268/625 [===========>..................] - ETA: 39s - loss: 0.6876 - binary_accuracy: 0.6163"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "270/625 [===========>..................] - ETA: 39s - loss: 0.6875 - binary_accuracy: 0.6167"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "271/625 [============>.................] - ETA: 39s - loss: 0.6875 - binary_accuracy: 0.6172"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "275/625 [============>.................] - ETA: 39s - loss: 0.6873 - binary_accuracy: 0.6193"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "277/625 [============>.................] - ETA: 38s - loss: 0.6873 - binary_accuracy: 0.6192"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "280/625 [============>.................] - ETA: 38s - loss: 0.6871 - binary_accuracy: 0.6211"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "281/625 [============>.................] - ETA: 38s - loss: 0.6871 - binary_accuracy: 0.6212"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "284/625 [============>.................] - ETA: 37s - loss: 0.6870 - binary_accuracy: 0.6224"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "288/625 [============>.................] - ETA: 36s - loss: 0.6869 - binary_accuracy: 0.6237"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "291/625 [============>.................] - ETA: 36s - loss: 0.6868 - binary_accuracy: 0.6246"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "293/625 [=============>................] - ETA: 36s - loss: 0.6868 - binary_accuracy: 0.6247"
     ]
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "301/625 [=============>................] - ETA: 34s - loss: 0.6865 - binary_accuracy: 0.6272"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "302/625 [=============>................] - ETA: 34s - loss: 0.6864 - binary_accuracy: 0.6276"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "306/625 [=============>................] - ETA: 33s - loss: 0.6863 - binary_accuracy: 0.6283"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "307/625 [=============>................] - ETA: 33s - loss: 0.6863 - binary_accuracy: 0.6289"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "308/625 [=============>................] - ETA: 33s - loss: 0.6862 - binary_accuracy: 0.6294"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "311/625 [=============>................] - ETA: 33s - loss: 0.6861 - binary_accuracy: 0.6301"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "315/625 [==============>...............] - ETA: 32s - loss: 0.6860 - binary_accuracy: 0.6305"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "321/625 [==============>...............] - ETA: 31s - loss: 0.6858 - binary_accuracy: 0.6317"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "327/625 [==============>...............] - ETA: 30s - loss: 0.6855 - binary_accuracy: 0.6332"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "329/625 [==============>...............] - ETA: 30s - loss: 0.6854 - binary_accuracy: 0.6340"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "330/625 [==============>...............] - ETA: 30s - loss: 0.6854 - binary_accuracy: 0.6344"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "333/625 [==============>...............] - ETA: 29s - loss: 0.6852 - binary_accuracy: 0.6353"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "338/625 [===============>..............] - ETA: 28s - loss: 0.6851 - binary_accuracy: 0.6362"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "339/625 [===============>..............] - ETA: 28s - loss: 0.6850 - binary_accuracy: 0.6369"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "343/625 [===============>..............] - ETA: 28s - loss: 0.6848 - binary_accuracy: 0.6378"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "348/625 [===============>..............] - ETA: 27s - loss: 0.6846 - binary_accuracy: 0.6383"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "349/625 [===============>..............] - ETA: 27s - loss: 0.6846 - binary_accuracy: 0.6383"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "360/625 [================>.............] - ETA: 25s - loss: 0.6841 - binary_accuracy: 0.6405"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "369/625 [================>.............] - ETA: 24s - loss: 0.6837 - binary_accuracy: 0.6433"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "370/625 [================>.............] - ETA: 24s - loss: 0.6836 - binary_accuracy: 0.6435"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "374/625 [================>.............] - ETA: 23s - loss: 0.6834 - binary_accuracy: 0.6451"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "378/625 [=================>............] - ETA: 23s - loss: 0.6832 - binary_accuracy: 0.6464"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "380/625 [=================>............] - ETA: 23s - loss: 0.6831 - binary_accuracy: 0.6475"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "390/625 [=================>............] - ETA: 21s - loss: 0.6825 - binary_accuracy: 0.6509"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "391/625 [=================>............] - ETA: 21s - loss: 0.6824 - binary_accuracy: 0.6515"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "392/625 [=================>............] - ETA: 21s - loss: 0.6824 - binary_accuracy: 0.6516"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "394/625 [=================>............] - ETA: 21s - loss: 0.6822 - binary_accuracy: 0.6523"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "401/625 [==================>...........] - ETA: 20s - loss: 0.6819 - binary_accuracy: 0.6536"
     ]
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "403/625 [==================>...........] - ETA: 20s - loss: 0.6817 - binary_accuracy: 0.6539"
     ]
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "407/625 [==================>...........] - ETA: 19s - loss: 0.6814 - binary_accuracy: 0.6553"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "409/625 [==================>...........] - ETA: 19s - loss: 0.6813 - binary_accuracy: 0.6559"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "410/625 [==================>...........] - ETA: 19s - loss: 0.6812 - binary_accuracy: 0.6563"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "418/625 [===================>..........] - ETA: 18s - loss: 0.6808 - binary_accuracy: 0.6580"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "421/625 [===================>..........] - ETA: 18s - loss: 0.6806 - binary_accuracy: 0.6586"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "427/625 [===================>..........] - ETA: 17s - loss: 0.6803 - binary_accuracy: 0.6603"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "437/625 [===================>..........] - ETA: 16s - loss: 0.6798 - binary_accuracy: 0.6616"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "445/625 [====================>.........] - ETA: 15s - loss: 0.6793 - binary_accuracy: 0.6636"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "446/625 [====================>.........] - ETA: 15s - loss: 0.6792 - binary_accuracy: 0.6638"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "447/625 [====================>.........] - ETA: 15s - loss: 0.6792 - binary_accuracy: 0.6638"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "451/625 [====================>.........] - ETA: 15s - loss: 0.6790 - binary_accuracy: 0.6646"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "454/625 [====================>.........] - ETA: 14s - loss: 0.6787 - binary_accuracy: 0.6654"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "456/625 [====================>.........] - ETA: 14s - loss: 0.6786 - binary_accuracy: 0.6658"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "464/625 [=====================>........] - ETA: 13s - loss: 0.6782 - binary_accuracy: 0.6668"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "465/625 [=====================>........] - ETA: 13s - loss: 0.6781 - binary_accuracy: 0.6674"
     ]
    },
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "470/625 [=====================>........] - ETA: 13s - loss: 0.6778 - binary_accuracy: 0.6678"
     ]
    },
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "474/625 [=====================>........] - ETA: 12s - loss: 0.6776 - binary_accuracy: 0.6681"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "488/625 [======================>.......] - ETA: 11s - loss: 0.6766 - binary_accuracy: 0.6709"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "490/625 [======================>.......] - ETA: 11s - loss: 0.6764 - binary_accuracy: 0.6714"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "497/625 [======================>.......] - ETA: 10s - loss: 0.6760 - binary_accuracy: 0.6727"
     ]
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "499/625 [======================>.......] - ETA: 10s - loss: 0.6758 - binary_accuracy: 0.6730"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "509/625 [=======================>......] - ETA: 9s - loss: 0.6751 - binary_accuracy: 0.6749 "
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "513/625 [=======================>......] - ETA: 8s - loss: 0.6749 - binary_accuracy: 0.6751"
     ]
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "521/625 [========================>.....] - ETA: 8s - loss: 0.6743 - binary_accuracy: 0.6768"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "524/625 [========================>.....] - ETA: 7s - loss: 0.6742 - binary_accuracy: 0.6767"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "526/625 [========================>.....] - ETA: 7s - loss: 0.6740 - binary_accuracy: 0.6774"
     ]
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "530/625 [========================>.....] - ETA: 7s - loss: 0.6738 - binary_accuracy: 0.6778"
     ]
    },
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "533/625 [========================>.....] - ETA: 7s - loss: 0.6736 - binary_accuracy: 0.6783"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "535/625 [========================>.....] - ETA: 7s - loss: 0.6735 - binary_accuracy: 0.6784"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "538/625 [========================>.....] - ETA: 6s - loss: 0.6733 - binary_accuracy: 0.6788"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "541/625 [========================>.....] - ETA: 6s - loss: 0.6731 - binary_accuracy: 0.6795"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "544/625 [=========================>....] - ETA: 6s - loss: 0.6729 - binary_accuracy: 0.6799"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "546/625 [=========================>....] - ETA: 6s - loss: 0.6727 - binary_accuracy: 0.6800"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "547/625 [=========================>....] - ETA: 6s - loss: 0.6726 - binary_accuracy: 0.6804"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "560/625 [=========================>....] - ETA: 4s - loss: 0.6717 - binary_accuracy: 0.6819"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "564/625 [==========================>...] - ETA: 4s - loss: 0.6714 - binary_accuracy: 0.6821"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "572/625 [==========================>...] - ETA: 4s - loss: 0.6708 - binary_accuracy: 0.6837"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "577/625 [==========================>...] - ETA: 3s - loss: 0.6705 - binary_accuracy: 0.6841"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "582/625 [==========================>...] - ETA: 3s - loss: 0.6702 - binary_accuracy: 0.6847"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "591/625 [===========================>..] - ETA: 2s - loss: 0.6695 - binary_accuracy: 0.6861"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "600/625 [===========================>..] - ETA: 1s - loss: 0.6687 - binary_accuracy: 0.6879"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "602/625 [===========================>..] - ETA: 1s - loss: 0.6684 - binary_accuracy: 0.6883"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "608/625 [============================>.] - ETA: 1s - loss: 0.6680 - binary_accuracy: 0.6892"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "609/625 [============================>.] - ETA: 1s - loss: 0.6679 - binary_accuracy: 0.6894"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "615/625 [============================>.] - ETA: 0s - loss: 0.6674 - binary_accuracy: 0.6902"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "619/625 [============================>.] - ETA: 0s - loss: 0.6672 - binary_accuracy: 0.6905"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "625/625 [==============================] - 48s 73ms/step - loss: 0.6668 - binary_accuracy: 0.6913 - val_loss: 0.6190 - val_binary_accuracy: 0.7690\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/625 [..............................] - ETA: 2s - loss: 0.5866 - binary_accuracy: 0.8438"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 17/625 [..............................] - ETA: 1s - loss: 0.6133 - binary_accuracy: 0.7739"
     ]
    },
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 33/625 [>.............................] - ETA: 1s - loss: 0.6118 - binary_accuracy: 0.7727"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 49/625 [=>............................] - ETA: 1s - loss: 0.6095 - binary_accuracy: 0.7730"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 65/625 [==>...........................] - ETA: 1s - loss: 0.6085 - binary_accuracy: 0.7697"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 82/625 [==>...........................] - ETA: 1s - loss: 0.6070 - binary_accuracy: 0.7664"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 99/625 [===>..........................] - ETA: 1s - loss: 0.6058 - binary_accuracy: 0.7667"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "150/625 [======>.......................] - ETA: 1s - loss: 0.6014 - binary_accuracy: 0.7688"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "166/625 [======>.......................] - ETA: 1s - loss: 0.5994 - binary_accuracy: 0.7722"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "182/625 [=======>......................] - ETA: 1s - loss: 0.5981 - binary_accuracy: 0.7716"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "198/625 [========>.....................] - ETA: 1s - loss: 0.5971 - binary_accuracy: 0.7702"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "214/625 [=========>....................] - ETA: 1s - loss: 0.5950 - binary_accuracy: 0.7728"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "230/625 [==========>...................] - ETA: 1s - loss: 0.5927 - binary_accuracy: 0.7746"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "246/625 [==========>...................] - ETA: 1s - loss: 0.5906 - binary_accuracy: 0.7755"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "262/625 [===========>..................] - ETA: 1s - loss: 0.5885 - binary_accuracy: 0.7768"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "278/625 [============>.................] - ETA: 1s - loss: 0.5860 - binary_accuracy: 0.7793"
     ]
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    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "294/625 [=============>................] - ETA: 1s - loss: 0.5853 - binary_accuracy: 0.7786"
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "310/625 [=============>................] - ETA: 0s - loss: 0.5839 - binary_accuracy: 0.7782"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "326/625 [==============>...............] - ETA: 0s - loss: 0.5827 - binary_accuracy: 0.7783"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "343/625 [===============>..............] - ETA: 0s - loss: 0.5812 - binary_accuracy: 0.7792"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "360/625 [================>.............] - ETA: 0s - loss: 0.5796 - binary_accuracy: 0.7809"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "377/625 [=================>............] - ETA: 0s - loss: 0.5777 - binary_accuracy: 0.7823"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "393/625 [=================>............] - ETA: 0s - loss: 0.5751 - binary_accuracy: 0.7845"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "409/625 [==================>...........] - ETA: 0s - loss: 0.5731 - binary_accuracy: 0.7861"
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    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "425/625 [===================>..........] - ETA: 0s - loss: 0.5714 - binary_accuracy: 0.7868"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "441/625 [====================>.........] - ETA: 0s - loss: 0.5696 - binary_accuracy: 0.7881"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "457/625 [====================>.........] - ETA: 0s - loss: 0.5680 - binary_accuracy: 0.7893"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "473/625 [=====================>........] - ETA: 0s - loss: 0.5666 - binary_accuracy: 0.7901"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "521/625 [========================>.....] - ETA: 0s - loss: 0.5612 - binary_accuracy: 0.7937"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "537/625 [========================>.....] - ETA: 0s - loss: 0.5601 - binary_accuracy: 0.7941"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "554/625 [=========================>....] - ETA: 0s - loss: 0.5584 - binary_accuracy: 0.7948"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "571/625 [==========================>...] - ETA: 0s - loss: 0.5568 - binary_accuracy: 0.7957"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "588/625 [===========================>..] - ETA: 0s - loss: 0.5556 - binary_accuracy: 0.7959"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "604/625 [===========================>..] - ETA: 0s - loss: 0.5539 - binary_accuracy: 0.7968"
     ]
    },
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "621/625 [============================>.] - ETA: 0s - loss: 0.5525 - binary_accuracy: 0.7975"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "625/625 [==============================] - 2s 4ms/step - loss: 0.5520 - binary_accuracy: 0.7976 - val_loss: 0.5009 - val_binary_accuracy: 0.8206\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/625 [..............................] - ETA: 3s - loss: 0.4370 - binary_accuracy: 0.8750"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 17/625 [..............................] - ETA: 1s - loss: 0.4881 - binary_accuracy: 0.8346"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 33/625 [>.............................] - ETA: 1s - loss: 0.4873 - binary_accuracy: 0.8286"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 50/625 [=>............................] - ETA: 1s - loss: 0.4867 - binary_accuracy: 0.8269"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 67/625 [==>...........................] - ETA: 1s - loss: 0.4852 - binary_accuracy: 0.8279"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 83/625 [==>...........................] - ETA: 1s - loss: 0.4830 - binary_accuracy: 0.8234"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 99/625 [===>..........................] - ETA: 1s - loss: 0.4838 - binary_accuracy: 0.8248"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "115/625 [====>.........................] - ETA: 1s - loss: 0.4817 - binary_accuracy: 0.8274"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "131/625 [=====>........................] - ETA: 1s - loss: 0.4806 - binary_accuracy: 0.8275"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "147/625 [======>.......................] - ETA: 1s - loss: 0.4808 - binary_accuracy: 0.8253"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "163/625 [======>.......................] - ETA: 1s - loss: 0.4800 - binary_accuracy: 0.8269"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "179/625 [=======>......................] - ETA: 1s - loss: 0.4777 - binary_accuracy: 0.8275"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "195/625 [========>.....................] - ETA: 1s - loss: 0.4780 - binary_accuracy: 0.8264"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "211/625 [=========>....................] - ETA: 1s - loss: 0.4767 - binary_accuracy: 0.8283"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "227/625 [=========>....................] - ETA: 1s - loss: 0.4748 - binary_accuracy: 0.8285"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "244/625 [==========>...................] - ETA: 1s - loss: 0.4728 - binary_accuracy: 0.8295"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "260/625 [===========>..................] - ETA: 1s - loss: 0.4713 - binary_accuracy: 0.8309"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "276/625 [============>.................] - ETA: 1s - loss: 0.4685 - binary_accuracy: 0.8334"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "292/625 [=============>................] - ETA: 1s - loss: 0.4694 - binary_accuracy: 0.8320"
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "308/625 [=============>................] - ETA: 1s - loss: 0.4683 - binary_accuracy: 0.8321"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "324/625 [==============>...............] - ETA: 0s - loss: 0.4681 - binary_accuracy: 0.8317"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "340/625 [===============>..............] - ETA: 0s - loss: 0.4670 - binary_accuracy: 0.8331"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "388/625 [=================>............] - ETA: 0s - loss: 0.4627 - binary_accuracy: 0.8357"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "404/625 [==================>...........] - ETA: 0s - loss: 0.4608 - binary_accuracy: 0.8369"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "420/625 [===================>..........] - ETA: 0s - loss: 0.4592 - binary_accuracy: 0.8378"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "436/625 [===================>..........] - ETA: 0s - loss: 0.4581 - binary_accuracy: 0.8384"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "452/625 [====================>.........] - ETA: 0s - loss: 0.4569 - binary_accuracy: 0.8388"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "468/625 [=====================>........] - ETA: 0s - loss: 0.4553 - binary_accuracy: 0.8403"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "484/625 [======================>.......] - ETA: 0s - loss: 0.4549 - binary_accuracy: 0.8401"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "500/625 [=======================>......] - ETA: 0s - loss: 0.4532 - binary_accuracy: 0.8414"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "517/625 [=======================>......] - ETA: 0s - loss: 0.4519 - binary_accuracy: 0.8421"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "534/625 [========================>.....] - ETA: 0s - loss: 0.4508 - binary_accuracy: 0.8423"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "551/625 [=========================>....] - ETA: 0s - loss: 0.4502 - binary_accuracy: 0.8427"
     ]
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "568/625 [==========================>...] - ETA: 0s - loss: 0.4491 - binary_accuracy: 0.8430"
     ]
    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "584/625 [===========================>..] - ETA: 0s - loss: 0.4485 - binary_accuracy: 0.8430"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "600/625 [===========================>..] - ETA: 0s - loss: 0.4475 - binary_accuracy: 0.8434"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "616/625 [============================>.] - ETA: 0s - loss: 0.4468 - binary_accuracy: 0.8434"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "625/625 [==============================] - 2s 4ms/step - loss: 0.4463 - binary_accuracy: 0.8432 - val_loss: 0.4213 - val_binary_accuracy: 0.8466\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/625 [..............................] - ETA: 2s - loss: 0.3591 - binary_accuracy: 0.9062"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 17/625 [..............................] - ETA: 1s - loss: 0.3984 - binary_accuracy: 0.8585"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 33/625 [>.............................] - ETA: 1s - loss: 0.3983 - binary_accuracy: 0.8636"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 49/625 [=>............................] - ETA: 1s - loss: 0.3995 - binary_accuracy: 0.8635"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 65/625 [==>...........................] - ETA: 1s - loss: 0.4012 - binary_accuracy: 0.8615"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 81/625 [==>...........................] - ETA: 1s - loss: 0.4005 - binary_accuracy: 0.8600"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 97/625 [===>..........................] - ETA: 1s - loss: 0.4026 - binary_accuracy: 0.8573"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "113/625 [====>.........................] - ETA: 1s - loss: 0.3997 - binary_accuracy: 0.8590"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "130/625 [=====>........................] - ETA: 1s - loss: 0.4004 - binary_accuracy: 0.8579"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "147/625 [======>.......................] - ETA: 1s - loss: 0.3997 - binary_accuracy: 0.8565"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "163/625 [======>.......................] - ETA: 1s - loss: 0.3995 - binary_accuracy: 0.8572"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "180/625 [=======>......................] - ETA: 1s - loss: 0.3978 - binary_accuracy: 0.8580"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "196/625 [========>.....................] - ETA: 1s - loss: 0.3985 - binary_accuracy: 0.8570"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "213/625 [=========>....................] - ETA: 1s - loss: 0.3976 - binary_accuracy: 0.8596"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "230/625 [==========>...................] - ETA: 1s - loss: 0.3960 - binary_accuracy: 0.8602"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "247/625 [==========>...................] - ETA: 1s - loss: 0.3952 - binary_accuracy: 0.8597"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "263/625 [===========>..................] - ETA: 1s - loss: 0.3935 - binary_accuracy: 0.8613"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "279/625 [============>.................] - ETA: 1s - loss: 0.3917 - binary_accuracy: 0.8627"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "295/625 [=============>................] - ETA: 1s - loss: 0.3942 - binary_accuracy: 0.8602"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "311/625 [=============>................] - ETA: 0s - loss: 0.3931 - binary_accuracy: 0.8597"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "327/625 [==============>...............] - ETA: 0s - loss: 0.3929 - binary_accuracy: 0.8599"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "344/625 [===============>..............] - ETA: 0s - loss: 0.3918 - binary_accuracy: 0.8601"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "360/625 [================>.............] - ETA: 0s - loss: 0.3911 - binary_accuracy: 0.8604"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "377/625 [=================>............] - ETA: 0s - loss: 0.3902 - binary_accuracy: 0.8604"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "393/625 [=================>............] - ETA: 0s - loss: 0.3883 - binary_accuracy: 0.8620"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "409/625 [==================>...........] - ETA: 0s - loss: 0.3870 - binary_accuracy: 0.8627"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "425/625 [===================>..........] - ETA: 0s - loss: 0.3861 - binary_accuracy: 0.8629"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "442/625 [====================>.........] - ETA: 0s - loss: 0.3845 - binary_accuracy: 0.8637"
     ]
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    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "458/625 [====================>.........] - ETA: 0s - loss: 0.3843 - binary_accuracy: 0.8639"
     ]
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    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "475/625 [=====================>........] - ETA: 0s - loss: 0.3842 - binary_accuracy: 0.8640"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "491/625 [======================>.......] - ETA: 0s - loss: 0.3827 - binary_accuracy: 0.8650"
     ]
    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "507/625 [=======================>......] - ETA: 0s - loss: 0.3815 - binary_accuracy: 0.8658"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "523/625 [========================>.....] - ETA: 0s - loss: 0.3807 - binary_accuracy: 0.8659"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "539/625 [========================>.....] - ETA: 0s - loss: 0.3807 - binary_accuracy: 0.8655"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "555/625 [=========================>....] - ETA: 0s - loss: 0.3803 - binary_accuracy: 0.8657"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "571/625 [==========================>...] - ETA: 0s - loss: 0.3794 - binary_accuracy: 0.8659"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "588/625 [===========================>..] - ETA: 0s - loss: 0.3797 - binary_accuracy: 0.8654"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "605/625 [============================>.] - ETA: 0s - loss: 0.3794 - binary_accuracy: 0.8659"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "621/625 [============================>.] - ETA: 0s - loss: 0.3793 - binary_accuracy: 0.8655"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "625/625 [==============================] - 2s 4ms/step - loss: 0.3789 - binary_accuracy: 0.8654 - val_loss: 0.3744 - val_binary_accuracy: 0.8616\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/625 [..............................] - ETA: 2s - loss: 0.3414 - binary_accuracy: 0.9062"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 17/625 [..............................] - ETA: 1s - loss: 0.3461 - binary_accuracy: 0.8842"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 34/625 [>.............................] - ETA: 1s - loss: 0.3481 - binary_accuracy: 0.8759"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 50/625 [=>............................] - ETA: 1s - loss: 0.3489 - binary_accuracy: 0.8731"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 66/625 [==>...........................] - ETA: 1s - loss: 0.3481 - binary_accuracy: 0.8736"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 82/625 [==>...........................] - ETA: 1s - loss: 0.3491 - binary_accuracy: 0.8712"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 98/625 [===>..........................] - ETA: 1s - loss: 0.3509 - binary_accuracy: 0.8715"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "245/625 [==========>...................] - ETA: 1s - loss: 0.3471 - binary_accuracy: 0.8741"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "262/625 [===========>..................] - ETA: 1s - loss: 0.3460 - binary_accuracy: 0.8757"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "279/625 [============>.................] - ETA: 1s - loss: 0.3438 - binary_accuracy: 0.8775"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "296/625 [=============>................] - ETA: 1s - loss: 0.3464 - binary_accuracy: 0.8753"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "312/625 [=============>................] - ETA: 0s - loss: 0.3463 - binary_accuracy: 0.8750"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "345/625 [===============>..............] - ETA: 0s - loss: 0.3450 - binary_accuracy: 0.8752"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "394/625 [=================>............] - ETA: 0s - loss: 0.3419 - binary_accuracy: 0.8766"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "411/625 [==================>...........] - ETA: 0s - loss: 0.3410 - binary_accuracy: 0.8769"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "428/625 [===================>..........] - ETA: 0s - loss: 0.3397 - binary_accuracy: 0.8777"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "445/625 [====================>.........] - ETA: 0s - loss: 0.3389 - binary_accuracy: 0.8777"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "462/625 [=====================>........] - ETA: 0s - loss: 0.3389 - binary_accuracy: 0.8774"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "527/625 [========================>.....] - ETA: 0s - loss: 0.3362 - binary_accuracy: 0.8783"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "611/625 [============================>.] - ETA: 0s - loss: 0.3361 - binary_accuracy: 0.8783"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "625/625 [==============================] - 2s 4ms/step - loss: 0.3358 - binary_accuracy: 0.8783 - val_loss: 0.3458 - val_binary_accuracy: 0.8662\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/625 [..............................] - ETA: 2s - loss: 0.2853 - binary_accuracy: 0.9062"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 18/625 [..............................] - ETA: 1s - loss: 0.3082 - binary_accuracy: 0.8976"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 35/625 [>.............................] - ETA: 1s - loss: 0.3119 - binary_accuracy: 0.8893"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 51/625 [=>............................] - ETA: 1s - loss: 0.3121 - binary_accuracy: 0.8860"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 67/625 [==>...........................] - ETA: 1s - loss: 0.3116 - binary_accuracy: 0.8881"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 83/625 [==>...........................] - ETA: 1s - loss: 0.3114 - binary_accuracy: 0.8882"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "100/625 [===>..........................] - ETA: 1s - loss: 0.3178 - binary_accuracy: 0.8844"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "117/625 [====>.........................] - ETA: 1s - loss: 0.3147 - binary_accuracy: 0.8851"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "134/625 [=====>........................] - ETA: 1s - loss: 0.3138 - binary_accuracy: 0.8848"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "150/625 [======>.......................] - ETA: 1s - loss: 0.3155 - binary_accuracy: 0.8842"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "167/625 [=======>......................] - ETA: 1s - loss: 0.3148 - binary_accuracy: 0.8853"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "184/625 [=======>......................] - ETA: 1s - loss: 0.3127 - binary_accuracy: 0.8871"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "201/625 [========>.....................] - ETA: 1s - loss: 0.3145 - binary_accuracy: 0.8846"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "217/625 [=========>....................] - ETA: 1s - loss: 0.3140 - binary_accuracy: 0.8854"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "233/625 [==========>...................] - ETA: 1s - loss: 0.3151 - binary_accuracy: 0.8848"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "250/625 [===========>..................] - ETA: 1s - loss: 0.3133 - binary_accuracy: 0.8851"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "267/625 [===========>..................] - ETA: 1s - loss: 0.3125 - binary_accuracy: 0.8872"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "284/625 [============>.................] - ETA: 1s - loss: 0.3124 - binary_accuracy: 0.8880"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "301/625 [=============>................] - ETA: 1s - loss: 0.3141 - binary_accuracy: 0.8861"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "318/625 [==============>...............] - ETA: 0s - loss: 0.3140 - binary_accuracy: 0.8858"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "335/625 [===============>..............] - ETA: 0s - loss: 0.3134 - binary_accuracy: 0.8851"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "352/625 [===============>..............] - ETA: 0s - loss: 0.3131 - binary_accuracy: 0.8848"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "369/625 [================>.............] - ETA: 0s - loss: 0.3123 - binary_accuracy: 0.8853"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "386/625 [=================>............] - ETA: 0s - loss: 0.3120 - binary_accuracy: 0.8855"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "403/625 [==================>...........] - ETA: 0s - loss: 0.3102 - binary_accuracy: 0.8863"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "420/625 [===================>..........] - ETA: 0s - loss: 0.3093 - binary_accuracy: 0.8868"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "436/625 [===================>..........] - ETA: 0s - loss: 0.3084 - binary_accuracy: 0.8870"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "452/625 [====================>.........] - ETA: 0s - loss: 0.3078 - binary_accuracy: 0.8872"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "468/625 [=====================>........] - ETA: 0s - loss: 0.3068 - binary_accuracy: 0.8878"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "484/625 [======================>.......] - ETA: 0s - loss: 0.3077 - binary_accuracy: 0.8871"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "501/625 [=======================>......] - ETA: 0s - loss: 0.3063 - binary_accuracy: 0.8882"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "518/625 [=======================>......] - ETA: 0s - loss: 0.3056 - binary_accuracy: 0.8889"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "535/625 [========================>.....] - ETA: 0s - loss: 0.3060 - binary_accuracy: 0.8883"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "552/625 [=========================>....] - ETA: 0s - loss: 0.3059 - binary_accuracy: 0.8885"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "569/625 [==========================>...] - ETA: 0s - loss: 0.3052 - binary_accuracy: 0.8889"
     ]
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "586/625 [===========================>..] - ETA: 0s - loss: 0.3061 - binary_accuracy: 0.8887"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "603/625 [===========================>..] - ETA: 0s - loss: 0.3061 - binary_accuracy: 0.8888"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "619/625 [============================>.] - ETA: 0s - loss: 0.3066 - binary_accuracy: 0.8883"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "625/625 [==============================] - 2s 4ms/step - loss: 0.3062 - binary_accuracy: 0.8885 - val_loss: 0.3267 - val_binary_accuracy: 0.8714\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/625 [..............................] - ETA: 2s - loss: 0.2947 - binary_accuracy: 0.9062"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 17/625 [..............................] - ETA: 1s - loss: 0.2857 - binary_accuracy: 0.9099"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 33/625 [>.............................] - ETA: 1s - loss: 0.2841 - binary_accuracy: 0.9072"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 49/625 [=>............................] - ETA: 1s - loss: 0.2841 - binary_accuracy: 0.9005"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 66/625 [==>...........................] - ETA: 1s - loss: 0.2855 - binary_accuracy: 0.9006"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 83/625 [==>...........................] - ETA: 1s - loss: 0.2873 - binary_accuracy: 0.8976"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "100/625 [===>..........................] - ETA: 1s - loss: 0.2932 - binary_accuracy: 0.8938"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "117/625 [====>.........................] - ETA: 1s - loss: 0.2895 - binary_accuracy: 0.8956"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "134/625 [=====>........................] - ETA: 1s - loss: 0.2880 - binary_accuracy: 0.8948"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "151/625 [======>.......................] - ETA: 1s - loss: 0.2898 - binary_accuracy: 0.8930"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "168/625 [=======>......................] - ETA: 1s - loss: 0.2889 - binary_accuracy: 0.8936"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "185/625 [=======>......................] - ETA: 1s - loss: 0.2876 - binary_accuracy: 0.8946"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "202/625 [========>.....................] - ETA: 1s - loss: 0.2890 - binary_accuracy: 0.8937"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "219/625 [=========>....................] - ETA: 1s - loss: 0.2875 - binary_accuracy: 0.8945"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "236/625 [==========>...................] - ETA: 1s - loss: 0.2901 - binary_accuracy: 0.8934"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "253/625 [===========>..................] - ETA: 1s - loss: 0.2891 - binary_accuracy: 0.8935"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "270/625 [===========>..................] - ETA: 1s - loss: 0.2871 - binary_accuracy: 0.8956"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "287/625 [============>.................] - ETA: 1s - loss: 0.2884 - binary_accuracy: 0.8951"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "304/625 [=============>................] - ETA: 0s - loss: 0.2889 - binary_accuracy: 0.8944"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "321/625 [==============>...............] - ETA: 0s - loss: 0.2891 - binary_accuracy: 0.8943"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "338/625 [===============>..............] - ETA: 0s - loss: 0.2882 - binary_accuracy: 0.8946"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "354/625 [===============>..............] - ETA: 0s - loss: 0.2877 - binary_accuracy: 0.8945"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "371/625 [================>.............] - ETA: 0s - loss: 0.2866 - binary_accuracy: 0.8951"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "388/625 [=================>............] - ETA: 0s - loss: 0.2860 - binary_accuracy: 0.8956"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "405/625 [==================>...........] - ETA: 0s - loss: 0.2848 - binary_accuracy: 0.8960"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "422/625 [===================>..........] - ETA: 0s - loss: 0.2835 - binary_accuracy: 0.8969"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "438/625 [====================>.........] - ETA: 0s - loss: 0.2829 - binary_accuracy: 0.8974"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "455/625 [====================>.........] - ETA: 0s - loss: 0.2824 - binary_accuracy: 0.8973"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "472/625 [=====================>........] - ETA: 0s - loss: 0.2828 - binary_accuracy: 0.8974"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "489/625 [======================>.......] - ETA: 0s - loss: 0.2823 - binary_accuracy: 0.8977"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "506/625 [=======================>......] - ETA: 0s - loss: 0.2812 - binary_accuracy: 0.8980"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "523/625 [========================>.....] - ETA: 0s - loss: 0.2807 - binary_accuracy: 0.8984"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "540/625 [========================>.....] - ETA: 0s - loss: 0.2813 - binary_accuracy: 0.8976"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "557/625 [=========================>....] - ETA: 0s - loss: 0.2809 - binary_accuracy: 0.8980"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "575/625 [==========================>...] - ETA: 0s - loss: 0.2811 - binary_accuracy: 0.8979"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "592/625 [===========================>..] - ETA: 0s - loss: 0.2816 - binary_accuracy: 0.8978"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "609/625 [============================>.] - ETA: 0s - loss: 0.2825 - binary_accuracy: 0.8975"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "625/625 [==============================] - 2s 3ms/step - loss: 0.2819 - binary_accuracy: 0.8975 - val_loss: 0.3132 - val_binary_accuracy: 0.8724\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/625 [..............................] - ETA: 2s - loss: 0.2643 - binary_accuracy: 0.9062"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 18/625 [..............................] - ETA: 1s - loss: 0.2610 - binary_accuracy: 0.9167"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 35/625 [>.............................] - ETA: 1s - loss: 0.2637 - binary_accuracy: 0.9116"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 52/625 [=>............................] - ETA: 1s - loss: 0.2648 - binary_accuracy: 0.9056"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 68/625 [==>...........................] - ETA: 1s - loss: 0.2659 - binary_accuracy: 0.9049"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 84/625 [===>..........................] - ETA: 1s - loss: 0.2661 - binary_accuracy: 0.9048"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "100/625 [===>..........................] - ETA: 1s - loss: 0.2739 - binary_accuracy: 0.9009"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "116/625 [====>.........................] - ETA: 1s - loss: 0.2693 - binary_accuracy: 0.9019"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "132/625 [=====>........................] - ETA: 1s - loss: 0.2691 - binary_accuracy: 0.9003"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "148/625 [======>.......................] - ETA: 1s - loss: 0.2703 - binary_accuracy: 0.8991"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "165/625 [======>.......................] - ETA: 1s - loss: 0.2704 - binary_accuracy: 0.8992"
     ]
    },
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "181/625 [=======>......................] - ETA: 1s - loss: 0.2681 - binary_accuracy: 0.9004"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "197/625 [========>.....................] - ETA: 1s - loss: 0.2698 - binary_accuracy: 0.8993"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "213/625 [=========>....................] - ETA: 1s - loss: 0.2693 - binary_accuracy: 0.9007"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "230/625 [==========>...................] - ETA: 1s - loss: 0.2686 - binary_accuracy: 0.9010"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "247/625 [==========>...................] - ETA: 1s - loss: 0.2685 - binary_accuracy: 0.9004"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "263/625 [===========>..................] - ETA: 1s - loss: 0.2678 - binary_accuracy: 0.9016"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "280/625 [============>.................] - ETA: 1s - loss: 0.2660 - binary_accuracy: 0.9031"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "296/625 [=============>................] - ETA: 1s - loss: 0.2687 - binary_accuracy: 0.9020"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "312/625 [=============>................] - ETA: 0s - loss: 0.2686 - binary_accuracy: 0.9023"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "328/625 [==============>...............] - ETA: 0s - loss: 0.2677 - binary_accuracy: 0.9025"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "345/625 [===============>..............] - ETA: 0s - loss: 0.2672 - binary_accuracy: 0.9024"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "362/625 [================>.............] - ETA: 0s - loss: 0.2666 - binary_accuracy: 0.9025"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "378/625 [=================>............] - ETA: 0s - loss: 0.2660 - binary_accuracy: 0.9028"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "394/625 [=================>............] - ETA: 0s - loss: 0.2647 - binary_accuracy: 0.9032"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "411/625 [==================>...........] - ETA: 0s - loss: 0.2642 - binary_accuracy: 0.9034"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "428/625 [===================>..........] - ETA: 0s - loss: 0.2634 - binary_accuracy: 0.9038"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "445/625 [====================>.........] - ETA: 0s - loss: 0.2628 - binary_accuracy: 0.9039"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "462/625 [=====================>........] - ETA: 0s - loss: 0.2629 - binary_accuracy: 0.9035"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "479/625 [=====================>........] - ETA: 0s - loss: 0.2636 - binary_accuracy: 0.9032"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "496/625 [======================>.......] - ETA: 0s - loss: 0.2624 - binary_accuracy: 0.9039"
     ]
    },
    {
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "513/625 [=======================>......] - ETA: 0s - loss: 0.2613 - binary_accuracy: 0.9048"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "529/625 [========================>.....] - ETA: 0s - loss: 0.2616 - binary_accuracy: 0.9041"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "545/625 [=========================>....] - ETA: 0s - loss: 0.2623 - binary_accuracy: 0.9041"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "562/625 [=========================>....] - ETA: 0s - loss: 0.2616 - binary_accuracy: 0.9049"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "580/625 [==========================>...] - ETA: 0s - loss: 0.2625 - binary_accuracy: 0.9046"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "597/625 [===========================>..] - ETA: 0s - loss: 0.2627 - binary_accuracy: 0.9047"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "614/625 [============================>.] - ETA: 0s - loss: 0.2632 - binary_accuracy: 0.9042"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "625/625 [==============================] - 2s 4ms/step - loss: 0.2632 - binary_accuracy: 0.9040 - val_loss: 0.3038 - val_binary_accuracy: 0.8754\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/625 [..............................] - ETA: 2s - loss: 0.2523 - binary_accuracy: 0.9375"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 18/625 [..............................] - ETA: 1s - loss: 0.2414 - binary_accuracy: 0.9236"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 35/625 [>.............................] - ETA: 1s - loss: 0.2462 - binary_accuracy: 0.9143"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 51/625 [=>............................] - ETA: 1s - loss: 0.2475 - binary_accuracy: 0.9112"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 68/625 [==>...........................] - ETA: 1s - loss: 0.2474 - binary_accuracy: 0.9118"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 85/625 [===>..........................] - ETA: 1s - loss: 0.2471 - binary_accuracy: 0.9110"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "102/625 [===>..........................] - ETA: 1s - loss: 0.2526 - binary_accuracy: 0.9096"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "136/625 [=====>........................] - ETA: 1s - loss: 0.2487 - binary_accuracy: 0.9099"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "221/625 [=========>....................] - ETA: 1s - loss: 0.2497 - binary_accuracy: 0.9087"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "238/625 [==========>...................] - ETA: 1s - loss: 0.2514 - binary_accuracy: 0.9074"
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "255/625 [===========>..................] - ETA: 1s - loss: 0.2509 - binary_accuracy: 0.9070"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "272/625 [============>.................] - ETA: 1s - loss: 0.2495 - binary_accuracy: 0.9081"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "289/625 [============>.................] - ETA: 1s - loss: 0.2512 - binary_accuracy: 0.9074"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "306/625 [=============>................] - ETA: 0s - loss: 0.2518 - binary_accuracy: 0.9077"
     ]
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "323/625 [==============>...............] - ETA: 0s - loss: 0.2517 - binary_accuracy: 0.9076"
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "340/625 [===============>..............] - ETA: 0s - loss: 0.2501 - binary_accuracy: 0.9077"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "357/625 [================>.............] - ETA: 0s - loss: 0.2498 - binary_accuracy: 0.9072"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "374/625 [================>.............] - ETA: 0s - loss: 0.2491 - binary_accuracy: 0.9081"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "391/625 [=================>............] - ETA: 0s - loss: 0.2482 - binary_accuracy: 0.9086"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "408/625 [==================>...........] - ETA: 0s - loss: 0.2474 - binary_accuracy: 0.9090"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "425/625 [===================>..........] - ETA: 0s - loss: 0.2470 - binary_accuracy: 0.9088"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "442/625 [====================>.........] - ETA: 0s - loss: 0.2455 - binary_accuracy: 0.9095"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "459/625 [=====================>........] - ETA: 0s - loss: 0.2457 - binary_accuracy: 0.9096"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "476/625 [=====================>........] - ETA: 0s - loss: 0.2468 - binary_accuracy: 0.9089"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "493/625 [======================>.......] - ETA: 0s - loss: 0.2454 - binary_accuracy: 0.9096"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "510/625 [=======================>......] - ETA: 0s - loss: 0.2443 - binary_accuracy: 0.9103"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "527/625 [========================>.....] - ETA: 0s - loss: 0.2444 - binary_accuracy: 0.9099"
     ]
    },
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "544/625 [=========================>....] - ETA: 0s - loss: 0.2448 - binary_accuracy: 0.9098"
     ]
    },
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "561/625 [=========================>....] - ETA: 0s - loss: 0.2446 - binary_accuracy: 0.9101"
     ]
    },
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "578/625 [==========================>...] - ETA: 0s - loss: 0.2454 - binary_accuracy: 0.9096"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "595/625 [===========================>..] - ETA: 0s - loss: 0.2459 - binary_accuracy: 0.9096"
     ]
    },
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "612/625 [============================>.] - ETA: 0s - loss: 0.2466 - binary_accuracy: 0.9093"
     ]
    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "625/625 [==============================] - 2s 3ms/step - loss: 0.2464 - binary_accuracy: 0.9093 - val_loss: 0.2971 - val_binary_accuracy: 0.8786\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/625 [..............................] - ETA: 2s - loss: 0.2288 - binary_accuracy: 0.9375"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 18/625 [..............................] - ETA: 1s - loss: 0.2367 - binary_accuracy: 0.9288"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 35/625 [>.............................] - ETA: 1s - loss: 0.2363 - binary_accuracy: 0.9196"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 51/625 [=>............................] - ETA: 1s - loss: 0.2371 - binary_accuracy: 0.9173"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 68/625 [==>...........................] - ETA: 1s - loss: 0.2355 - binary_accuracy: 0.9177"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 84/625 [===>..........................] - ETA: 1s - loss: 0.2341 - binary_accuracy: 0.9182"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "100/625 [===>..........................] - ETA: 1s - loss: 0.2423 - binary_accuracy: 0.9153"
     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "117/625 [====>.........................] - ETA: 1s - loss: 0.2382 - binary_accuracy: 0.9164"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "134/625 [=====>........................] - ETA: 1s - loss: 0.2369 - binary_accuracy: 0.9163"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "151/625 [======>.......................] - ETA: 1s - loss: 0.2386 - binary_accuracy: 0.9145"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "167/625 [=======>......................] - ETA: 1s - loss: 0.2381 - binary_accuracy: 0.9147"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "184/625 [=======>......................] - ETA: 1s - loss: 0.2356 - binary_accuracy: 0.9153"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "200/625 [========>.....................] - ETA: 1s - loss: 0.2376 - binary_accuracy: 0.9144"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "217/625 [=========>....................] - ETA: 1s - loss: 0.2369 - binary_accuracy: 0.9149"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "234/625 [==========>...................] - ETA: 1s - loss: 0.2387 - binary_accuracy: 0.9139"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "251/625 [===========>..................] - ETA: 1s - loss: 0.2368 - binary_accuracy: 0.9138"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "267/625 [===========>..................] - ETA: 1s - loss: 0.2371 - binary_accuracy: 0.9141"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "284/625 [============>.................] - ETA: 1s - loss: 0.2363 - binary_accuracy: 0.9152"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "300/625 [=============>................] - ETA: 1s - loss: 0.2380 - binary_accuracy: 0.9142"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "317/625 [==============>...............] - ETA: 0s - loss: 0.2384 - binary_accuracy: 0.9141"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "334/625 [===============>..............] - ETA: 0s - loss: 0.2373 - binary_accuracy: 0.9144"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "350/625 [===============>..............] - ETA: 0s - loss: 0.2367 - binary_accuracy: 0.9137"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "366/625 [================>.............] - ETA: 0s - loss: 0.2356 - binary_accuracy: 0.9144"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "382/625 [=================>............] - ETA: 0s - loss: 0.2353 - binary_accuracy: 0.9146"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "398/625 [==================>...........] - ETA: 0s - loss: 0.2347 - binary_accuracy: 0.9145"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "414/625 [==================>...........] - ETA: 0s - loss: 0.2342 - binary_accuracy: 0.9153"
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    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "431/625 [===================>..........] - ETA: 0s - loss: 0.2331 - binary_accuracy: 0.9157"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "448/625 [====================>.........] - ETA: 0s - loss: 0.2327 - binary_accuracy: 0.9157"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "464/625 [=====================>........] - ETA: 0s - loss: 0.2326 - binary_accuracy: 0.9158"
     ]
    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "496/625 [======================>.......] - ETA: 0s - loss: 0.2323 - binary_accuracy: 0.9158"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "512/625 [=======================>......] - ETA: 0s - loss: 0.2314 - binary_accuracy: 0.9167"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "529/625 [========================>.....] - ETA: 0s - loss: 0.2315 - binary_accuracy: 0.9162"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "545/625 [=========================>....] - ETA: 0s - loss: 0.2318 - binary_accuracy: 0.9162"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "561/625 [=========================>....] - ETA: 0s - loss: 0.2312 - binary_accuracy: 0.9165"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "577/625 [==========================>...] - ETA: 0s - loss: 0.2319 - binary_accuracy: 0.9162"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "593/625 [===========================>..] - ETA: 0s - loss: 0.2324 - binary_accuracy: 0.9162"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "609/625 [============================>.] - ETA: 0s - loss: 0.2334 - binary_accuracy: 0.9155"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "625/625 [==============================] - ETA: 0s - loss: 0.2329 - binary_accuracy: 0.9156"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "625/625 [==============================] - 2s 4ms/step - loss: 0.2329 - binary_accuracy: 0.9156 - val_loss: 0.2926 - val_binary_accuracy: 0.8782\n"
     ]
    }
   ],
   "source": [
    "epochs = 10\n",
    "history = model.fit(\n",
    "    train_ds,\n",
    "    validation_data=val_ds,\n",
    "    epochs=epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9EEGuDVuzb5r"
   },
   "source": [
    "### 评估模型\n",
    "\n",
    "我们来看一下模型的性能如何。将返回两个值。损失值（loss）（一个表示误差的数字，值越低越好）与准确率（accuracy）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:08:18.127359Z",
     "iopub.status.busy": "2023-11-08T00:08:18.126730Z",
     "iopub.status.idle": "2023-11-08T00:08:19.768022Z",
     "shell.execute_reply": "2023-11-08T00:08:19.767150Z"
    },
    "id": "zOMKywn4zReN"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/782 [..............................] - ETA: 1:00 - loss: 0.3673 - binary_accuracy: 0.9062"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 26/782 [..............................] - ETA: 1s - loss: 0.3163 - binary_accuracy: 0.8654  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 52/782 [>.............................] - ETA: 1s - loss: 0.3239 - binary_accuracy: 0.8594"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 78/782 [=>............................] - ETA: 1s - loss: 0.3201 - binary_accuracy: 0.8578"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "104/782 [==>...........................] - ETA: 1s - loss: 0.3188 - binary_accuracy: 0.8624"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "129/782 [===>..........................] - ETA: 1s - loss: 0.3172 - binary_accuracy: 0.8656"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "155/782 [====>.........................] - ETA: 1s - loss: 0.3166 - binary_accuracy: 0.8665"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "181/782 [=====>........................] - ETA: 1s - loss: 0.3135 - binary_accuracy: 0.8684"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "207/782 [======>.......................] - ETA: 1s - loss: 0.3151 - binary_accuracy: 0.8685"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "233/782 [=======>......................] - ETA: 1s - loss: 0.3134 - binary_accuracy: 0.8700"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "258/782 [========>.....................] - ETA: 1s - loss: 0.3148 - binary_accuracy: 0.8708"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "283/782 [=========>....................] - ETA: 0s - loss: 0.3124 - binary_accuracy: 0.8720"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "310/782 [==========>...................] - ETA: 0s - loss: 0.3112 - binary_accuracy: 0.8739"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "336/782 [===========>..................] - ETA: 0s - loss: 0.3111 - binary_accuracy: 0.8743"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "362/782 [============>.................] - ETA: 0s - loss: 0.3117 - binary_accuracy: 0.8734"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "388/782 [=============>................] - ETA: 0s - loss: 0.3104 - binary_accuracy: 0.8740"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "414/782 [==============>...............] - ETA: 0s - loss: 0.3087 - binary_accuracy: 0.8749"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "440/782 [===============>..............] - ETA: 0s - loss: 0.3099 - binary_accuracy: 0.8742"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "466/782 [================>.............] - ETA: 0s - loss: 0.3104 - binary_accuracy: 0.8737"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "492/782 [=================>............] - ETA: 0s - loss: 0.3109 - binary_accuracy: 0.8733"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "517/782 [==================>...........] - ETA: 0s - loss: 0.3105 - binary_accuracy: 0.8733"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "543/782 [===================>..........] - ETA: 0s - loss: 0.3098 - binary_accuracy: 0.8741"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "569/782 [====================>.........] - ETA: 0s - loss: 0.3085 - binary_accuracy: 0.8745"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "595/782 [=====================>........] - ETA: 0s - loss: 0.3094 - binary_accuracy: 0.8740"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "621/782 [======================>.......] - ETA: 0s - loss: 0.3096 - binary_accuracy: 0.8740"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "647/782 [=======================>......] - ETA: 0s - loss: 0.3093 - binary_accuracy: 0.8742"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "672/782 [========================>.....] - ETA: 0s - loss: 0.3098 - binary_accuracy: 0.8737"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "698/782 [=========================>....] - ETA: 0s - loss: 0.3097 - binary_accuracy: 0.8736"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "724/782 [==========================>...] - ETA: 0s - loss: 0.3096 - binary_accuracy: 0.8735"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "749/782 [===========================>..] - ETA: 0s - loss: 0.3094 - binary_accuracy: 0.8735"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "774/782 [============================>.] - ETA: 0s - loss: 0.3111 - binary_accuracy: 0.8728"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "782/782 [==============================] - 2s 2ms/step - loss: 0.3107 - binary_accuracy: 0.8732\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss:  0.310722291469574\n",
      "Accuracy:  0.8731600046157837\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = model.evaluate(test_ds)\n",
    "\n",
    "print(\"Loss: \", loss)\n",
    "print(\"Accuracy: \", accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "z1iEXVTR0Z2t"
   },
   "source": [
    "这种十分朴素的方法得到了约 87% 的准确率（accuracy）。若采用更好的方法，模型的准确率应当接近 95%。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ldbQqCw2Xc1W"
   },
   "source": [
    "### 创建准确率和损失随时间变化的图表\n",
    "\n",
    "`model.fit()` 会返回包含一个字典的 `History` 对象。该字典包含训练过程中产生的所有信息："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:08:19.771969Z",
     "iopub.status.busy": "2023-11-08T00:08:19.771334Z",
     "iopub.status.idle": "2023-11-08T00:08:19.776308Z",
     "shell.execute_reply": "2023-11-08T00:08:19.775552Z"
    },
    "id": "-YcvZsdvWfDf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['loss', 'binary_accuracy', 'val_loss', 'val_binary_accuracy'])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history_dict = history.history\n",
    "history_dict.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1_CH32qJXruI"
   },
   "source": [
    "其中有四个条目：每个条目代表训练和验证过程中的一项监测指标。您可以使用这些指标来绘制用于比较的训练损失和验证损失图表，以及训练准确率和验证准确率图表："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:08:19.780029Z",
     "iopub.status.busy": "2023-11-08T00:08:19.779288Z",
     "iopub.status.idle": "2023-11-08T00:08:19.939379Z",
     "shell.execute_reply": "2023-11-08T00:08:19.938685Z"
    },
    "id": "2SEMeQ5YXs8z"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = history_dict['binary_accuracy']\n",
    "val_acc = history_dict['val_binary_accuracy']\n",
    "loss = history_dict['loss']\n",
    "val_loss = history_dict['val_loss']\n",
    "\n",
    "epochs = range(1, len(acc) + 1)\n",
    "\n",
    "# \"bo\" is for \"blue dot\"\n",
    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "# b is for \"solid blue line\"\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:08:19.943145Z",
     "iopub.status.busy": "2023-11-08T00:08:19.942687Z",
     "iopub.status.idle": "2023-11-08T00:08:20.095342Z",
     "shell.execute_reply": "2023-11-08T00:08:20.094616Z"
    },
    "id": "Z3PJemLPXwz_"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hFFyCuJoXy7r"
   },
   "source": [
    "在该图表中，虚线代表训练损失和准确率，实线代表验证损失和准确率。\n",
    "\n",
    "请注意，训练损失会逐周期*下降*，而训练准确率则逐周期*上升*。使用梯度下降优化时，这是预期结果，它应该在每次迭代中最大限度减少所需的数量。\n",
    "\n",
    "但是，对于验证损失和准确率来说则不然——它们似乎会在训练转确率之前达到顶点。这是过拟合的一个例子：模型在训练数据上的表现要好于在之前从未见过的数据上的表现。经过这一点之后，模型会过度优化和学习*特定*于训练数据的表示，但无法*泛化*到测试数据。\n",
    "\n",
    "对于这种特殊情况，您可以通过在验证准确率不再增加时直接停止训练来防止过度拟合。一种方式是使用 `tf.keras.callbacks.EarlyStopping` 回调。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-to23J3Vy5d3"
   },
   "source": [
    "## 导出模型\n",
    "\n",
    "在上面的代码中，您在向模型馈送文本之前对数据集应用了 `TextVectorization`。 如果您想让模型能够处理原始字符串（例如，为了简化部署），您可以在模型中包含 `TextVectorization` 层。为此，您可以使用刚刚训练的权重创建一个新模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:08:20.099491Z",
     "iopub.status.busy": "2023-11-08T00:08:20.098808Z",
     "iopub.status.idle": "2023-11-08T00:08:23.463040Z",
     "shell.execute_reply": "2023-11-08T00:08:23.462257Z"
    },
    "id": "FWXsMvryuZuq"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/782 [..............................] - ETA: 4:24 - loss: 0.1804 - accuracy: 0.9375"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 14/782 [..............................] - ETA: 3s - loss: 0.3199 - accuracy: 0.8638  "
     ]
    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 28/782 [>.............................] - ETA: 2s - loss: 0.3452 - accuracy: 0.8449"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 42/782 [>.............................] - ETA: 2s - loss: 0.3181 - accuracy: 0.8631"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 56/782 [=>............................] - ETA: 2s - loss: 0.3271 - accuracy: 0.8577"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 70/782 [=>............................] - ETA: 2s - loss: 0.3193 - accuracy: 0.8598"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 84/782 [==>...........................] - ETA: 2s - loss: 0.3235 - accuracy: 0.8583"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 98/782 [==>...........................] - ETA: 2s - loss: 0.3178 - accuracy: 0.8597"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "112/782 [===>..........................] - ETA: 2s - loss: 0.3139 - accuracy: 0.8641"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "126/782 [===>..........................] - ETA: 2s - loss: 0.3175 - accuracy: 0.8646"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "140/782 [====>.........................] - ETA: 2s - loss: 0.3179 - accuracy: 0.8650"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "154/782 [====>.........................] - ETA: 2s - loss: 0.3150 - accuracy: 0.8667"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "168/782 [=====>........................] - ETA: 2s - loss: 0.3182 - accuracy: 0.8655"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "182/782 [=====>........................] - ETA: 2s - loss: 0.3152 - accuracy: 0.8678"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "196/782 [======>.......................] - ETA: 2s - loss: 0.3131 - accuracy: 0.8689"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "210/782 [=======>......................] - ETA: 2s - loss: 0.3136 - accuracy: 0.8699"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "224/782 [=======>......................] - ETA: 2s - loss: 0.3148 - accuracy: 0.8684"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "238/782 [========>.....................] - ETA: 2s - loss: 0.3146 - accuracy: 0.8699"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "252/782 [========>.....................] - ETA: 1s - loss: 0.3153 - accuracy: 0.8704"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "266/782 [=========>....................] - ETA: 1s - loss: 0.3137 - accuracy: 0.8721"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "280/782 [=========>....................] - ETA: 1s - loss: 0.3128 - accuracy: 0.8720"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "293/782 [==========>...................] - ETA: 1s - loss: 0.3118 - accuracy: 0.8724"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "306/782 [==========>...................] - ETA: 1s - loss: 0.3113 - accuracy: 0.8732"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "320/782 [===========>..................] - ETA: 1s - loss: 0.3123 - accuracy: 0.8729"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "334/782 [===========>..................] - ETA: 1s - loss: 0.3121 - accuracy: 0.8734"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "348/782 [============>.................] - ETA: 1s - loss: 0.3119 - accuracy: 0.8733"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "361/782 [============>.................] - ETA: 1s - loss: 0.3110 - accuracy: 0.8736"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "375/782 [=============>................] - ETA: 1s - loss: 0.3111 - accuracy: 0.8739"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "389/782 [=============>................] - ETA: 1s - loss: 0.3109 - accuracy: 0.8738"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "403/782 [==============>...............] - ETA: 1s - loss: 0.3096 - accuracy: 0.8745"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "416/782 [==============>...............] - ETA: 1s - loss: 0.3094 - accuracy: 0.8742"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "429/782 [===============>..............] - ETA: 1s - loss: 0.3100 - accuracy: 0.8738"
     ]
    },
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "443/782 [===============>..............] - ETA: 1s - loss: 0.3095 - accuracy: 0.8737"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "456/782 [================>.............] - ETA: 1s - loss: 0.3102 - accuracy: 0.8738"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "470/782 [=================>............] - ETA: 1s - loss: 0.3106 - accuracy: 0.8737"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "483/782 [=================>............] - ETA: 1s - loss: 0.3112 - accuracy: 0.8736"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "497/782 [==================>...........] - ETA: 1s - loss: 0.3113 - accuracy: 0.8732"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "510/782 [==================>...........] - ETA: 1s - loss: 0.3112 - accuracy: 0.8732"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "524/782 [===================>..........] - ETA: 0s - loss: 0.3105 - accuracy: 0.8734"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "537/782 [===================>..........] - ETA: 0s - loss: 0.3098 - accuracy: 0.8742"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "551/782 [====================>.........] - ETA: 0s - loss: 0.3089 - accuracy: 0.8747"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "565/782 [====================>.........] - ETA: 0s - loss: 0.3094 - accuracy: 0.8741"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "578/782 [=====================>........] - ETA: 0s - loss: 0.3089 - accuracy: 0.8744"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "592/782 [=====================>........] - ETA: 0s - loss: 0.3091 - accuracy: 0.8741"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "605/782 [======================>.......] - ETA: 0s - loss: 0.3095 - accuracy: 0.8743"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "618/782 [======================>.......] - ETA: 0s - loss: 0.3094 - accuracy: 0.8741"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "632/782 [=======================>......] - ETA: 0s - loss: 0.3098 - accuracy: 0.8740"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "645/782 [=======================>......] - ETA: 0s - loss: 0.3099 - accuracy: 0.8739"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "659/782 [========================>.....] - ETA: 0s - loss: 0.3096 - accuracy: 0.8736"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "673/782 [========================>.....] - ETA: 0s - loss: 0.3101 - accuracy: 0.8735"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "687/782 [=========================>....] - ETA: 0s - loss: 0.3096 - accuracy: 0.8737"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "701/782 [=========================>....] - ETA: 0s - loss: 0.3096 - accuracy: 0.8734"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "714/782 [==========================>...] - ETA: 0s - loss: 0.3102 - accuracy: 0.8731"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "728/782 [==========================>...] - ETA: 0s - loss: 0.3095 - accuracy: 0.8735"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "742/782 [===========================>..] - ETA: 0s - loss: 0.3100 - accuracy: 0.8733"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "756/782 [============================>.] - ETA: 0s - loss: 0.3108 - accuracy: 0.8727"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "769/782 [============================>.] - ETA: 0s - loss: 0.3110 - accuracy: 0.8727"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "782/782 [==============================] - 3s 4ms/step - loss: 0.3107 - accuracy: 0.8732\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8731600046157837\n"
     ]
    }
   ],
   "source": [
    "export_model = tf.keras.Sequential([\n",
    "  vectorize_layer,\n",
    "  model,\n",
    "  layers.Activation('sigmoid')\n",
    "])\n",
    "\n",
    "export_model.compile(\n",
    "    loss=losses.BinaryCrossentropy(from_logits=False), optimizer=\"adam\", metrics=['accuracy']\n",
    ")\n",
    "\n",
    "# Test it with `raw_test_ds`, which yields raw strings\n",
    "loss, accuracy = export_model.evaluate(raw_test_ds)\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TwQgoN88LoEF"
   },
   "source": [
    "### 使用新数据进行推断\n",
    "\n",
    "要获得对新样本的预测，只需调用 `model.predict()` 即可。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:08:23.466999Z",
     "iopub.status.busy": "2023-11-08T00:08:23.466420Z",
     "iopub.status.idle": "2023-11-08T00:08:23.696099Z",
     "shell.execute_reply": "2023-11-08T00:08:23.695269Z"
    },
    "id": "QW355HH5L49K"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "1/1 [==============================] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1/1 [==============================] - 0s 177ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.5980279 ],\n",
       "       [0.41937438],\n",
       "       [0.33809566]], dtype=float32)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "examples = [\n",
    "  \"The movie was great!\",\n",
    "  \"The movie was okay.\",\n",
    "  \"The movie was terrible...\"\n",
    "]\n",
    "\n",
    "export_model.predict(examples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MaxlpFWpzR6c"
   },
   "source": [
    "将文本预处理逻辑包含在模型中后，您可以导出用于生产的模型，从而简化部署并降低[训练/测试偏差](https://developers.google.com/machine-learning/guides/rules-of-ml#training-serving_skew)的可能性。\n",
    "\n",
    "在选择应用 TextVectorization 层的位置时，需要注意性能差异。在模型之外使用它可以让您在 GPU 上训练时进行异步 CPU 处理和数据缓冲。因此，如果您在 GPU 上训练模型，您应该在开发模型时使用此选项以获得最佳性能，然后在准备好部署时进行切换，在模型中包含 TextVectorization 层。\n",
    "\n",
    "请参阅此[教程](https://tensorflow.google.cn/tutorials/keras/save_and_load)，详细了解如何保存模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eSSuci_6nCEG"
   },
   "source": [
    "## 练习：对 Stack Overflow 问题进行多类分类\n",
    "\n",
    "本教程展示了如何在 IMDB 数据集上从头开始训练二元分类器。作为练习，您可以修改此笔记本以训练多类分类器来预测 [Stack Overflow](http://stackoverflow.com/) 上的编程问题的标签。\n",
    "\n",
    "我们已经准备好了一个[数据集](https://storage.googleapis.com/download.tensorflow.org/data/stack_overflow_16k.tar.gz)供您使用，其中包含了几千个发布在 Stack Overflow 上的编程问题（例如，\"How can sort a dictionary by value in Python?\"）。每一个问题都只有一个标签（Python、CSharp、JavaScript 或 Java）。您的任务是将问题作为输入，并预测适当的标签，在本例中为 Python。\n",
    "\n",
    "您将使用的数据集包含从 [BigQuery](https://console.cloud.google.com/marketplace/details/stack-exchange/stack-overflow) 上更大的公共 Stack Overflow 数据集提取的数千个问题，其中包含超过 1700 万个帖子。\n",
    "\n",
    "下载数据集后，您会发现它与您之前使用的 IMDB 数据集具有相似的目录结构：\n",
    "\n",
    "```\n",
    "train/\n",
    "...python/\n",
    "......0.txt\n",
    "......1.txt\n",
    "...javascript/\n",
    "......0.txt\n",
    "......1.txt\n",
    "...csharp/\n",
    "......0.txt\n",
    "......1.txt\n",
    "...java/\n",
    "......0.txt\n",
    "......1.txt\n",
    "```\n",
    "\n",
    "注：为了增加分类问题的难度，编程问题中出现的 Python、CSharp、JavaScript 或 Java 等词已被替换为 *blank*（因为许多问题都包含它们所涉及的语言）。\n",
    "\n",
    "要完成此练习，您应该对此笔记本进行以下修改以使用 Stack Overflow 数据集：\n",
    "\n",
    "1. 在笔记本顶部，将下载 IMDB 数据集的代码更新为下载前面准备好的 [Stack Overflow 数据集](https://storage.googleapis.com/download.tensorflow.org/data/stack_overflow_16k.tar.gz)的代码。由于 Stack Overflow 数据集具有类似的目录结构，因此您不需要进行太多修改。\n",
    "\n",
    "2. 将模型的最后一层修改为 `Dense(4)`，因为现在有四个输出类。\n",
    "\n",
    "3. 编译模型时，将损失更改为 `tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)`。当每个类的标签是整数（在本例中，它们可以是 0、*1*、*2* 或 *3*）时，这是用于多类分类问题的正确损失函数。 此外，将指标更改为 `metrics=['accuracy']`，因为这是一个多类分类问题（`tf.metrics.BinaryAccuracy` 仅用于二元分类器 ）。\n",
    "\n",
    "4. 在绘制随时间变化的准确率时，请将 `binary_accuracy` 和 `val_binary_accuracy` 分别更改为 `accuracy` 和 `val_accuracy`。\n",
    "\n",
    "5. 完成这些更改后，就可以训练多类分类器了。 "
   ]
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   "source": [
    "## 了解更多信息\n",
    "\n",
    "本教程从头开始介绍了文本分类。要详细了解一般的文本分类工作流程，请查看 Google Developers 提供的[文本分类指南](https://developers.google.com/machine-learning/guides/text-classification/)。\n"
   ]
  }
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